esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Underwater Wireless Sensor Networks  
The UWSN is a network used to perform monitoring of tasks over a specific region; it is equipped with smart sensors and vehicles that are adapted to communicate cooperatively through wireless connections [8]. The surface sink retrieves the data from sensor nodes. The sink node has a transceiver that can control acoustic signals received from underwater nodes. The transceiver also can transmit and receive long-range radio frequency signals for communication with the onshore station. The collected data are used locally or connected to another network for a particular purpose [9]. Figure 2 illustrates an overview of the UWSN environment. The network architecture incorporates traditional underwater wireless sensor networks designed by [8] and real-time underwater wireless sensor network architecture in the form of Internet of Underwater Things proposed by [10].  
Underwater wireless sensor networks comprise of nodes that are deployable on the surface and under the water. All nodes need to communicate and exchange information with other nodes in the same network and with the base station. Communication systems in the sensor network involve the transmission of data using acoustic, electromagnetic, or optical wave media. Among these types of media, acoustic communication is the most popular and widely used method due to its attenuation features in water. The factor of low transmission is derived from absorption and conversion of energy into heat in water. Meanwhile, acoustic signals operate at low frequencies, which enables them to be transmitted and received over long distances.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviation** & **Description** \\ \hline IIoT & Industry Internet of Things \\ \hline IoT & Internet of Things \\ \hline IoUT & Internet of Underwater Things \\ \hline M2M & Machine-to-Machine \\ \hline Mbps & Megabits per second \\ \hline ROVs & Remotely Operative Underwater Vehicles \\ \hline SDN & Software Define Networking \\ \hline SOFAR & Sound Fixing and Ranging \\ \hline TTL & Time to live \\ \hline UWSNs & Underwater Wireless Sensor Networks \\ \hline WSN & Wireless Sensor Networks \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 2: Overview of Underwater Wireless Sensor Networks.
# Reaction Forces and Apparent Thrust in Dual Oscillating Control Moment Gyroscopes
## 3 Numerical Simulation
### Elementary Calculations  
Based on the abovementioned data, the total oscillating mass (gyroscope plus motor driver) is  
\[m=m_{\rm frame}+m_{\rm rotor}+m_{\rm driver}=33+112+200=345\,{\rm g}=0.345\,{ \rm kg}\,. \tag{41}\]  
The rotational inertia of the gyroscope's rotor is  
\[I_{\rm rotor} =\frac{1}{2}m_{\rm rotor}\Big{(}R_{\rm in}^{2}+R_{\rm out}^{2} \Big{)}\] \[=0.5\times 0.112\times((41.3/2000)^{2}+(53.0/2000)^{2})\] \[=6.3205\times 10^{-5}\left[{\rm kg}\,{\rm m}^{2}\right]. \tag{42}\]  
The angular momentum of the gyroscope's rotor is \[L_{\rm rotor} = I_{\rm rotor}\omega_{\rm spin} \tag{43}\] \[= (6.3205\times 10^{-5})\times(16,000\times\frac{\pi}{30})\] \[= 0.10590\,[{\rm kg\ m^{2}/s}]\,.\]  
Regarding the eccentricity \(r\) which is involved in Equations (18) and (19), the former is produced by the frame of mass \(m_{\rm gyro}=0.145\,{\rm kg}\) at a radius \(r_{\rm gyro}=0\), the (assumed cylindrical) motor of mass \(m_{\rm motor}=0.2\,{\rm kg}\), and the length \(l_{\rm motor}=0.0582\,{\rm m}\). Due to the radius of the frame at which the motor is attached, the center of mass of the aforementioned motor is eventually at a radius  
\[r_{\rm motor}=\frac{(D_{\rm out}+l_{\rm motor})}{2}=\frac{0.0625+0.0582}{2}=0.06035\,{\rm m}\,. \tag{44}\]  
Therefore, by applying the well-known rule for the center of mass of the system \"gyro + motor\", the latter will give an eccentricity equal to  
\[r=\frac{0.145\times 0+0.200\times 0.06035}{0.145+0.200}\approx 0.035\,{\rm m}\,, \tag{45}\]  
while the rotating eccentric mass is  
\[m=m_{\rm gyro}+m_{\rm motor}=0.145+0.2000=0.345\,{\rm kg}\,. \tag{46}\]  
Therefore, the useful magnitude is  
\[mr=0.345\times 0.035=0.01207\,[{\rm kgm}]\,. \tag{47}\]  
Alternatively, the same result could be obtained when considering only the rotating eccentric mass of \(m_{\rm driver}=0.2\,{\rm kg}\), which is at a distance \(r_{\rm motor}=0.06035\,{\rm m}\) such that \(m_{\rm driver}\times r_{\rm motor}=0.2\times 0.06035=0.01207\,[{\rm kgm}]\) (i.e., the same as Equation (47)).  
**Remark 2**.: _While the data employed in this work are adapted from a previously reported prototype study [22; 23; 24; 25], which involved a sophisticated design and integration process, it is important to underscore that the specific numerical values presented in Equations (41)-(43) are not critical to the overall conclusions of this paper; that is, the oscillating mass \(m\) has been chosen to be consistent with the associated angular momentum \(L_{\rm rotor}\) of the gyroscope's rotor. However, any other compatible pair \((m,L_{\rm rotor})\) would serve equally well._
## 4 Results and Discussion  
### Complex Network Development Based on AR  
In the present study, association rule mining is implemented by R 4.3.1 using the package \"arules\". Due to the data imbalance problem in the dataset, in order to focus on the less frequently occurring interactions among seafarers' unsafe acts and their influencing factors, this study set the minimum support to 0.01, the minimum confidence to 0.1, the lift to greater than 1, and the maximum length of the itemset to 2 after many trials. Finally, a total of 504 association rules are obtained. Then, the interactions between the LHS and RHS in these association rules are further analyzed, and the rules with inverse or unreasonable relationships are removed. Finally, 260 association rules are retained, as shown in Figure 4.  
Based on the generated 260 association rules, this study developed an interaction network of seafarers' unsafe acts and their influencing factors with 53 nodes and 260 directed edges, as shown in Figure 5. The circles represent the nodes and the arrows represent the directed edges.
# A Cyber-Physical System for Wildfire Detection and Firefighting
## 3 Materials and Methods  
In this section, we will dive into the details of the design and implementation of a Cyber-Physical System (CPS) for wildfire detection using ROS-based UAVs (Unmanned Aerial Vehicles) for firefighting and extinguishing. The goal of this CPS is to provide a comprehensive and effective solution for detecting and combating wildfires. The system will use a combination of unmanned systems, each equipped with different sensors and actuators, to gather data and perform various tasks related to firefighting. The UAVs will be responsible for collecting data about the wildfire, including its location, size, and direction of spread. They will also use thermal imaging cameras to identify hotspots and other areas of high temperature. This information will be transmitted to the UGVs, which will use it to plan and execute firefighting strategies. The UGVs will be equipped with various firefighting tools, such as water hoses and foam dispensers, to extinguish the wildfire. They will also be able to navigate through rough terrain, using sensors to detect obstacles and adjust their path accordingly. The CPS will be implemented using Robot Operating System (ROS), a widely used platform for developing robotic systems. This will allow for seamless communication and coordination between the UAVs and UGVs, ensuring that they work together efficiently to achieve the common goal of extinguishing wildfires. Overall, the design and implementation of this CPS will provide a powerful tool for detecting and combating wildfires, with the potential to save lives and prevent extensive damage to the environment.
## 3 Mathematical Models and Validations
### Battery Electrochemical Model  
The dual-potential, multi-scale, multi-domain (MSMD) and electric circuit model (ECM) were selected as the simulation methodologies in this study. The conventional Newman's electrochemistry model is simplified in Equation (1) [32]:  
\[\int\limits_{V}\
abla\cdot(\sigma\
abla\varnothing)dV=\int\limits_{A}jdA \tag{1}\]  
where \(\
abla\) is the Del operator, \(\phi\) is the electric potential, \(j\) is the volumetric transfer current density, \(A\) is the surface area of the interface, and \(\sigma\) is the electrical conductivity.  
For the MSMD model, the battery electrical and thermal fields are expressed by Equations (2)-(4) [33]:  
\[\frac{\partial\rho_{b}Cp_{b}T}{\partial t}-\
abla\cdot(k_{b}\
abla T)=\dot{q} \tag{2}\]  
\[\
abla\cdot(\sigma_{+}\
abla\varphi_{+})=-j \tag{3}\]  
\[\
abla\cdot(\sigma_{-}\
abla\varphi_{-})=j \tag{4}\]  
where \(\rho_{b}\) is the battery mass density, \(Cp_{b}\) is the battery specific heat capacity, \(T\) is the temperature, \(k_{b}\) is the battery thermal conductivity, \(\dot{q}\) is the heat generation rate which consists of the Joule heat, the electrochemical reaction heat, and the entropic heat, \(\sigma_{+}\) and \(\sigma_{-}\) are the anode and cathode effective electrical conductivities, and \(\varphi_{+}\) and \(\varphi_{-}\) are the anode and cathode phase potentials.  
For the ECM model, a diagram from the work of [PERSON] et al. [34] was proposed to illustrate its mechanism, as shown in Figure 3. It is a surrogate model with six parameters, including 3 resistors and 2 capacitors, to simulate the internal electrochemical behaviours of a LIB cell.  
The voltages and currents in the ECM can be solved by Equations (5)-(8) [34]:  
\[V(t)=V_{\text{OC}}(SoC)+V_{p}+V_{n}-R_{o}(SoC)I(t) \tag{5}\]  
\[\frac{dU_{p}}{dt}=-\frac{1}{R_{p}(SoC)C_{p}(SoC)}V_{p}-\frac{1}{C_{p}(SoC)}I(t) \tag{6}\]  
\[\frac{dU_{n}}{dt}=-\frac{1}{R_{n}(SoC)C_{n}(SoC)}V_{n}-\frac{1}{C_{n}(SoC)}I(t) \tag{7}\]  
\[\frac{d(SoC)}{dt}=\frac{I(t)}{3600Q_{b}} \tag{8}\]  
where \(V\) is the battery voltage, \(I\) is the battery current, \(V_{\text{OC}}\) is the open-circuit voltage, \(SoC\) is the state of charge, \(V_{p}\) and \(V_{n}\) are the voltages of \(R_{p}/C_{p}\) and \(R_{n}/C_{n}\), \(R_{o}\), \(R_{p}\) and \(R_{n}\) are the resistance of resistor o, \(p\), and \(n\), \(C_{p}\) and \(C_{n}\) are the capacitance of capacitor \(p\) and \(n\), \(Q_{b}\) is the total battery capacity.
## 2 Materials and Methods
### Fatigue Detection Framework and Methods
#### 2.1.3 TICC Clustering and Data Labelling  
We use the TICC clustering method to determine the fatigue level and complete the data labelling by taking the six features of KSS score, SSS score, BR, PERCLOS, AMV and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicators** & **Formula** & **Description** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Average Mouse \\ Velocity (AMV) \\ \end{tabular} } & \begin{tabular}{c} The ratio of the total distance of mouse movement in \\ the specified time interval to the total time of each \\ interval, \(dis_{D}\) is the Euclidean distance of the \(D\)th \\ mouse movement, \(t_{D}\) is the time interval of the \(D\)th \\ mouse movement process. \\ \end{tabular} \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Average Value \\ of Mouse \\ Velocity (AOV) \\ \end{tabular} } &
\begin{tabular}{c} The average of the velocities of two consecutive \\ mouse movement positions captured during the \\ specified time interval, \(dis_{D}\) is the Euclidean distance \\ of the \(D\)th mouse movement, \(t_{D}\) is the time interval of \\ the \(D\)th mouse movement process. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average mouse velocity and average value of the mouse velocity.  
AOV as inputs. The indicators are shown in Table 3. The change trend of these multi-feature time series reflects the fatigue level of operators at different levels.  
According to the method used for TICC in [30], we up-sampled the subjective scores separately, keeping the scores consistent for the same Time-On-Monitoring (TOM) task segment. Because the camera captures frame-level eye data, as well as the mouse coordinates captured by the Python script, the scale of change is less than 1 s. We chose 1 Hz as our resampling frequency. In order to ensure that the sampling frequency of features is consistent, we down-sampled the eye and mouse data, respectively, to obtain the blink rate per second, PERCLOS per second, average mouse velocity per second and the average value of mouse velocity per second. The above features are standardized. After preprocessing, a 6 \(\times\) T \(\times\) 1 multivariate time series is finally obtained.  
TICC finds repetitive patterns from the multivariate time series in the above and describes each sample according to the corresponding fatigue status. For using TICC, we set the window value to 1 (1 sample). The penalty factor \(\beta\) is chosen from the range of 100 to 400 intervals and the regularization parameter \(\lambda\) is chosen from the range of 0.01 to 0.5.
## 2 Application of the Space Syntax Analysis to an Urban Context
### Space Syntax Analysis Theoretical Concepts
The choice to refer to the angle is supported by scientific evidence in the cognitive field [36]. Space syntax analysis is suitable both for pedestrian and vehicular traffic forecasting while recent studies are even aimed at extending its validity to generic variables such as land use, property or financial rents, noise pollution [37; 38]. It must be underlined that more modelling problems appear with the vehicular traffic. For example, in an urban grid, roads in a traffic-restricted area must not be considered, and one-way roads represent a challenging modelling problem because at the present status the software does not consider the direction of travel of a road [39]. Another problem is the edge-effect because the analysis is not able to consider the traffic towards and from the city [40; 41].
###### Abstract  
In this study, functional particles such as piezoelectric (PZT) ceramic and shape memory alloy (SMA) particles have been incorporated in composite laminates to accelerate the loss of vibration energy. PZT ceramic particles and SMA particles are mixed with epoxy resin and rolled into a film shape before they are interleaved between prepreg plies for better distribution of the particles. Loss factor (tan \(\delta\)) was measured with various particle loadings to verify the effectiveness of interleaving in the vibration damping of laminate specimens. It was observed that there existed an optimal content for maximizing the damping ability avoiding an aggregation of the particles. In addition, when PZT and SMA particles are applied simultaneously, PZT could enhance the vibration damping capability of SMA because PZT particles could generate thermal energy, and it would accelerate the phase change of the SMA particles. In this research, the effective way for enhancing the particle dispersion was suggested, and the particle loading could be controlled by finding an optimal content. Flexural moduli of the specimens were also measured, and they exhibited no change as the content of the particles increases. Therefore, dispersed particles used in this study increased the vibration damping capacity without reducing the mechanical properties.  
lead zirconate titanate (PZT); shape memory alloy; resin film; polymer matrix composites (PMCs); vibration; loss factor; particle dispersion Article
# Simulation of Surface Topography Considering Cut-in Impact and Tool Flank Wear
## 8 Conclusions  
Based on the Z-MAP method, a prediction model of machined surface topography considering dynamic characteristics of the cutting system and tool flank wear is established. The effects of cut-in impact and tool wear on surface morphology and roughness are studied based on the theoretical model. The verification milling experiment was carried out on the hardened steel Cr12 MoV workpiece. The main results and conclusions are summarized as follows:  
1. The simulated surface texture and roughness are in good agreement with the experimental measurements, which verifies the correctness of the surface morphology prediction model. The reasons for the difference in surface morphology between the simulated and experimental are as follows: (1) the cut-in phase angle is random in the experiment process; (2) the surface topography model does not take into account the tool deformation; (3) the tool wear is assumed to be the same in the wear area in the simulation process.
2. The prediction model of surface topography considering cut-in impact has higher accuracy, and the error of surface roughness is reduced from 12.09% to 9.52%.  
Figure 14: Surface morphology at different tool wear, Ra(um) (**a**) experiment—0.356; simulation—0.372;(**b**) experiment—0.424; simulation—0.395.  
With the increase of tool wear, the texture structure of the workpiece surface changes and the maximum scallop height on the surface increases accordingly. Meanwhile, the texture asperities grow and the grooves deepen with tool wear in the experiments. The worn tool causes scratches on the machined surface, which leads to deterioration of the machined surface.
4. The surface roughness increases with the increase of tool wear. However, the growth rate of surface roughness has a nonlinear relationship with tool wear. When the tool wear is less than 0.164 mm, the surface roughness simulation results are in good agreement with the experimental results. When the tool wear is 0.21 mm, the maximum error between the surface roughness simulation and the experimental measurement is 21.9%. The reason is that the cutting edge near the tool tip induces micro-chipping.  
The idea of this project was conceived by [PERSON], [PERSON] and [PERSON] consulted to relevant high-level papers on aspects of surface topography, and wrote this project. [PERSON] and [PERSON] reviewed this project and proposed constructive guidance to make the article more complete.  
This project is supported by the National Natural Science Foundation of China (NSFC) (No. 51575147) and the Projects of International Cooperation and Exchanges NSFC (No. 51720105009), and the Science Funds for the Young Innovative Talents of HUST (No.201507).  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Sequencing, Assembly, and Annotation  
Whole plants were collected from Iksan city (geographic coordinate: 35\({}^{\circ}\)58\({}^{\prime}\)05\({}^{\prime\prime}\) N, 126\({}^{\circ}\)57\({}^{\prime}\)31\({}^{\prime\prime}\) E), Jeonbuk province, Korea. For DNA extraction, 3 g of young leaves was used, and DNA quality was tested using a NanoDrop(tm) Lite Spectro-photometer (Thermo Inc., Waltham, MA, USA). Passed DNA samples were quantified for Illumina paired-end (PE) library construction. The PE library was sequenced by a HiSeq X platform (Illumina Inc., San Diego, CA, USA) with 125 nucleotides in PE mode. To remove poor-quality sequences and used adapters, trimming was performed with a Phred quality score < 20. Constructed contigs were assembled and verified using a CLC Assembler (CLC Bio, Aarhus, Denmark) with an assembly accuracy test. The genome used as a reference for assembly was from _Adenophora remiflora_ (NCBI, accession number: OP920648). Online GeSeq [16] and Artemis tool (version 17) [17] were used for gene annotation, and multi-layer ring diagrams were created using CPGVienw (version 1.4.6) [18].
## 4 Theory of the Thermal Conductivity of Graphene and GNR
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{1}{c}{**Sample**} & \multicolumn{1}{c}{**\(K\) (W/mK)**} & \multicolumn{1}{c}{**Method**} & \multicolumn{1}{c}{**Description**} & \multicolumn{1}{c}{**Refs.**} \\ \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**Experimental Data**} \\ \hline \multirow{6}{*}{SLG} & \(\sim\)3000–5000 & Raman optothermal & Suspended; exfoliated & [4, 5] \\ \cline{2-5}  & 2500 & Raman optothermal & Suspended; chemical vapor deposition & [32] \\  & & & (CVD) grown & \\ \cline{2-5}  & 1500–5000 & Raman optothermal & Suspended; CVD grown & [33] \\ \cline{2-5}  & 600 & Raman optothermal & Suspended; exfoliated; T \(\sim\) 660 K & [99] \\ \cline{2-5}  & 600 & Electrical & Supported; exfoliated & [37] \\ \cline{2-5}  & 310–530 & Electrical & Exfoliated and chemical vapor deposition grown; & [100] \\  & & self-heating & \(T\sim\) 1000 K & \\ \hline \multirow{6}{*}{FLG} & 1300–2800 & Raman optothermal & Suspended; exfoliated; \(n=2\)–4 & [31] \\ \cline{2-5}  & 50–970 & Heat-spreader method & FLG, encased within SiO\({}_{2}\); \(n=2\),..., 21 & [101] \\ \cline{2-5}  & 150–1200 & Electrical self-heating & Suspended and supported FLG; polymeric residues & [102] \\ \cline{2-5}  & & on the surface & \\ \cline{2-5}  & 302–596 & Modified T-bridge & Suspended; \(n=2\)–8 & [103] \\ \hline Bilayer & 560–620 & Electrical self-heating & Suspended; polymeric residues on the surface & [104] \\ graphene & & & & \\ \hline FLG & 1100 & Electrical self-heating & Supported; exfoliated; \(n<5\) & [105] \\ \cline{2-5}  & 80–150 & Electrical self-heating & Supported & [106] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Thermal conductivity of graphene and graphene nanoribbons.
## 5 Results and Discussion  
Packet delivery ratio (PDR) is the percentage of packets arriving at the destination from the source. As the percentage of such packets increases, the protocol running on that network is said to work better. Figure 7 shows that the proposed protocol MSVPC has received a large number of packets in percentage format. This is due to the network design and the routing methods in it. In addition, in the comparable scalable virtual video optimizer network (SVNVO) and ALVN, it can be seen that the percentage of packets it has received is reduced due to the lack of optimization system routing designed in the proposed model. Here, the network shows 81.1448 percentage of packet delivery ratio in MSVPC. Because of the swarm optimization-based video transmission and multicast routing this performance enhancement is achieved.  
Network delay is the amount of time it takes to transfer video packets from source to destination. This includes transmission propagation delay, queuing delay and network delay. In Figure 8 the network delay is less than the proposed MSVPC method. The reason is the choice of network design and associated parameters. When the path for video transmission is selected in PSO mode, the delay is calculated as an important parameter and thus the local best node and global best nodes are selected and the path is set. This significantly reduces network delay and, as a result, more packets are delivered to the destination as in Figure 9. Network delay considers the queue, channel and propagation delay timings, and including all these timings MSVPC achieved 0.0694974 as a minimum delay compared to other protocols.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Network Parameters** \\ \hline Information Point & 100–200 \\ Access points & 5 \\ Internet Gateway & 5 \\ Streaming Server & 1 \\ No of Antenna & 3 \\ Modulation Type & QAM256 \\ Simulation Time & 200 \\ No of Channel & 10 \\ Multicast Group Members & 30 \\ Antenna Type & Beam forming MM Wave \\ Coverage & 80 \\ Communication Type & MIMO \\ Traffic Application & H265 Codec Video Streaming \\ Connection Type & UDP \\ Mac standard & IEEE P802.11 ay \\ Maximum Bandwidth & 100 Ghz \\ \hline \hline \end{tabular}
\end{table}
Table 5: Network parameters.  
of service output has increased in proposed method compared to SVNVO method available in the literature and may be benchmarked for transferring of data through the 5G network.  
In Table 6 the nodes, packet interval, packet sizes and simulation time are taken as inputs and the changes caused by its variations are calculated as results. The proposed protocol, MSVPC, has a higher packet delivery ratio than the other two protocols compared to it as well as less delay.