esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Super Resolution Combining Spatial Attention and Channel Attention
## 3 Proposed Method
### Attention Block  
Figure 11 is Attention of the proposed method. The high-frequency region in an image is the outline and texture information of an object and has a great influence on the visual quality of an image. This means that high-frequency region restoration is important in the super resolution process, and for this purpose, high-frequency features are emphasized using CSBlock that uses attention mechanism. It is possible to emphasize strong high-frequency features by connecting CSBlocks in series, and a skip connection structure is applied to solve the gradient vanishing problem that can occur in the deepened network due to serial connection. Through this structure, learning was conducted without emphasizing high-frequency features and without a gradient vanishing problem.  
\[AB(x)=H(CSBlock_{10}(CSBlock_{9}(\cdots CSBlock_{1}(x)\cdots))+x), \tag{12}\]  
Equation (12) is an attention block formula for emphasizing super resolution. \(AB(*)\) means attention block, \(H(*)\) means convolution operation, \(CSBlock_{n}(*)\) is the nth CSBlock, and \(x\) is the input feature map. The network used 10 CSBlocks and was composed of a skip connection structure and convolution. This structure helps network learning by preventing and supplementing gradient vanishing.
## 2 System and Channel Models
### System Model  
We consider a dual-hop SAGIN where the LEO satellite (S) communicates with the ground station (G) with the aid of an HAPS (H), as is shown in Figure 1. The direct S-G link is assumed to be blocked due to severe shadowing and atmospheric attenuation phenomena. In the proposed system, it is assumed that communication transmissions are performed in two orthogonal phases. In the first phase of communication, S transmits the signal to H using an FSO link. In that case, the received SNR at the HAPS is given by ([17] [Equation (7)]) (Without losing the generality, it is assumed that at the received SNR, subscript 1 denotes FSO links and subscript 2 denotes RF links)  
\[\gamma_{1}=\frac{\left(\eta P_{f}G_{Tf}G_{RF}I\right)^{b}}{F^{b}\sigma_{f}^{2 }}, \tag{1}\]  
where \(\eta\) denotes the optical-to-electrical conversion coefficient, \(P_{f}\) denotes the transmit power of the FSO communication system, \(G_{Tf},G_{RF}\) are the transmit and receive telescope gains, respectively, \(I\) denotes the random fluctuations of the received amplitude, \(F=(4\pi d_{k}/\lambda_{f})\), where \(\lambda_{f}\) is the wavelength of the FSO communications, and \(d_{k}\) denotes the transmission distance between the FSO transmitter and FSO receiver (with \(k\in s,h\)), as is also shown in Figure 1. Moreover, \(b=1\) and \(b=2\) for heterodyne and direct detection schemes, respectively, while \(\sigma_{f}^{2}\) denotes the noise variance of the additive white Gaussian noise (AWGN).  
The H acts as a relay and implements DF protocol. Therefore, in the second phase of communications, if H has correctly decoded the signal received from the satellite, it forwards it to the \(G\) using hybrid RF/FSO communications. The received FSO signal at the \(G\) is characterized by an instantaneous SNR of the form presented in (1).  
As far as the RF communication links are concerned, the instantaneous received SNR per symbol at the \(G\) is given by ([17] [Equation (12)])  
\[\gamma_{2}=\bar{\gamma}_{2}|h_{r}|^{2}, \tag{2}\]  
where \(\bar{\gamma}_{2}\) is defined as  
\[\bar{\gamma}_{2}=\frac{P_{t}}{N_{0}}\bigg{(}\frac{G_{Tt}G_{Rt}\lambda_{r}^{2} }{16\pi^{2}d_{h}^{\omega}}\bigg{)}. \tag{3}\]  
In (3), \(G_{Tt}\) and \(G_{Rt}\) denote the transmit and receive antenna gains for the RF systems, respectively, \(\lambda_{r}\) is the RF wavelength, and \(d_{h}\) denotes the H-G distance. Moreover, \(|h_{r}|\) denotes the normalized magnitude of the channel fading coefficient; \(|\cdot|\) denotes absolute value.
## 2 Research Methodology
### Energy Utilization Equation  
Based on the relationship between thermal deformation and temperature change, as well as the relationship between energy and temperature change, the equation for thermal deformation in terms of energy can be expressed as follows:  
\[\frac{\Delta L}{L}=\Delta T\cdot\alpha \tag{5}\]  
\[Q_{1}=cm\Delta T \tag{6}\]  
\[Q_{2}=Pt \tag{7}\]  
where \(\Delta L\) represents the displacement deformation of the part caused by thermal effects; \(\Delta T\) signifies the change in material temperature during the manufacturing process; \(\alpha\) denotes the coefficient of thermal expansion of the material (which depends solely on the material's inherent properties); \(c\) is the material's specific heat coefficient; \(m\) represents the mass of the part; \(Q_{1}\) denotes the heat absorbed by the part during the manufacturing process; and \(Q_{2}\) is the total energy released by the laser during the processing.  
The temperature distribution inside a metal can be described by the heat conduction equation [33]:  
\[\rho gcg\frac{\partial T}{\partial t}=\
abla g(kg\
abla T)+Q \tag{8}\]  
where \(\rho\) is the density of the metal, \(c\) is the specific heat capacity of the metal, \(k\) is the thermal conductivity, \(Q\) is the power density of laser absorbed by the metal per unit time, \(T\) is the temperature, and \(t\) is the time.  
The heat conduction equation inside the metal describes the manner and rate at which thermal energy is transferred within the metal, taking into account the thermal conductivity properties of the metal and temperature differences. This equation plays a fundamental role in the simulation and computational modeling of metal additive manufacturing processes.  
Through the displacement deformation of the part during processing, the temperature change of the part from powder to processing can be calculated, so as to obtain the absorbed power of the part. By calculating the total power, the energy utilization rate of the part processing process can finally be obtained.
# Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks
## 4 Methodology
### UAV Description  
The types of drones used in the simulation are essential to determine the performance of the solution apart from the network parameters. We assumed that the access and backhaul network equipment weighs about 1.5 kg; hence, the selection of drones are based on this parameter [40].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Sub 6 GHz Backhaul** & **mmWave Backhaul** \\ \hline Frequency & 3.5 GHz & 61.5 GHz \\ Bandwidth & 20 MHz & 9 GHz \\ Number of resource blocks & 100 & 45,000 \\ Number of used subcarriers & 1200 & 540,000 \\ Total number of subcarriers & 2048 & 1,048,576 \\ Max transmission power UABS & 43 dBm & 10 dBm \\ Max transmission power CN & 43 dBm & 10 dBm \\ Antenna gain UABS & 5 dBi & 36 dBi (2.5\({}^{\circ}\)) \\ Antenna gain CN & 5 dBi & 36 dBi (2.5\({}^{\circ}\)) \\ Fade margin & 10 dB & 5 dB \\ Interference margin & 2 dB & 2 dB \\  & 1/3 QPSK = –1.5 dB & \\  & 1/2 QPSK = 3 dB & \\ Receiver signal-to-noise ratio & 2/3 QPSK = 10.5 dB & 1/2 BPSK = 7.39 dB \\ (SNR) for the modulation and & 1/2 16-QAM = 14 dB & 1/2 QPSK = 15.4 dB \\ coding scheme (MCS) & 2/3 16-QAM = 19 dB & 1/2 16 QAM = 17.5 dB \\  & 1/2 64-QAM = 23 dB & \\  & 2/3 64-QAM = 29.4 dB & \\ Noise figure in UABS & 5 dB & 5 dB \\ Shadowing margin & 8.2 dB & 8.2 dB \\ MIMO gain & 0 dB & 0 dB \\ CN antenna height & & 25–60 m \\ User height & & 1.5 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Link budget parameters for the simulation.  
We proposed three types of drones for these simulations. The first type of drone was a commercial off-the-shelf hexacopter drone like the DJI F550, which its size and battery capacity were small and its flight time was approximately 15 min [41]. The second drone was a professional quadcopter like the MD4-100 able to carry more than 1 kg of payload and with enough battery capacity to flight nearly 45 min with the described payload [42]. The third drone was a Harris H4 Hybrid drone, which combined a traditional quadcopter drone with a 48 V battery with an H2000 generator of 1.8 KWatts. Both will add together a total of 37.5 Ah per hour, and with a consumption of 1.5 L/h from a four-liter tank, the total capacity available will be 100 Ah [43]. From the facility, 1500 drones were available to serve the emergency; so when a drone was running out of battery, a new drone from the facility flight to the old drone's position and replaced it until 1500 drones were used. Backhaul handover was considered seamless because it would happen less than four times during the intervention. All drone specifications are presented in Table 3.
## 5 Predefined Path-Based Techniques
### Comparison  
We described three predefined path-based techniques for reducing HSR unicast traffic. Characteristics of these techniques are shown in Table 4.  
The ODP approach establishes dual paths based on the network's link information, whereas the DVP and RDP approaches build dual paths by sending and receiving control messages. The ODP and DVP approaches setup node-based dual paths for each connection pair of terminal nodes such as DANH nodes, RedBox nodes, while the RDP establishes ring-based dual paths for each connection pair of DANH rings or terminal rings. Therefore, the RDP approach significantly reduces the number of connection pairs required to discover and establish dual paths.  
We consider a sample network consisting of three QuadBox rings and eight DANH rings; each DANH ring has four DANH nodes, as shown in Figure 12. Two paired QuadBoxes are used to connect two rings. The number of DANH nodes in the network is 32 nodes. Under the ODP and DVP approaches, the number of connection pairs of DANH nodes is \(32\times(32-1)/2=496\) connection pairs. Under the RDP approach, the number of connection pairs of DANH rings is \(8\times(8-1)/2=28\) connection pairs. Generally, the RDP approach has about \(m^{2}\) times less the number of connection pairs than the ODP and DVP approaches, where \(m\) is the number of terminal nodes in a ring. Therefore, the RDP significantly reduces the control overhead for discovering and establishing dual paths, as well as the memory space required to store the dual paths compared with the ODP and DVP approaches.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **ODP** & **DVP** & **RDP** \\ \hline Type of connection pair to setup dual paths & Node & Node & Ring \\ Using network topology database & Yes & No & No \\ Using control messages to setup dual paths & No & Yes & Yes \\ Pruning traffic for unused rings & Yes & Yes & Yes \\ Removing traffic circulation & Yes & Yes & Yes \\ Type of filtered traffic & Unicast & Unicast & Unicast \\ Network topology & Any & Any & Ring \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristics of predefined path-based techniques.  
To compare traffic performance of these techniques, we conducted several simulations using the OMNeT++ simulation tool. We consider the sample network as shown in Figure 12. In these simulations, a source in DANH ring 1 sends unicast frames to a destination in DANH ring 5.  
Network unicast frames recorded from the simulations under the standard HSR protocol, the ODP approach, the DVP approach, and the RDP approach are shown in Table 3. Figure 13 shows the comparison of traffic performance of these techniques. The simulation results show that, in the sample network, the predefined path-based techniques have the same network unicast traffic performance. These techniques significantly reduce network unicast traffic by about 80% compared with standard HSR protocol.
# An Efficient Ultra-Tight GPS/RISS Integrated System for Challenging Navigation Environments
## 6 Conclusions and Future Work  
The paper addresses the problem of navigation in challenging environments for land vehicle applications. A robust navigation system that uses an INS-aided vector-based signal tracking receiver was designed and implemented. The system was tested under one of the most severe challenging conditions for GPS receivers, signal jamming. Results show that the developed method--in contrast to scalar tracking systems and standalone vector-based GPS systems--performed well, even in the case of real of signal jamming. The proposed ultra-tight GPS/RISS system maintained an RMS 2D position error less than 10 m under a challenging scenario. Furthermore, this shows that RISS is a dependable alternative for full IMU configurations in land vehicle navigation applications. In addition to providing adequate accuracy, it comes with less complexity and minimal cost with respect to full IMU configurations.  
[PERSON] proposed the idea, conceived and designed the simulations, analyzed the data, and wrote the paper. [PERSON] and A.N. reviewed the paper and simulation results. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
These characteristics present significant challenges for large-scale AGB estimation with remote sensing technology, and there is currently a lack of targeted research in this specific area. Airborne LiDAR data offer a promising solution, as they can efficiently and accurately extract tree attributes such as height and crown width from point cloud data, which are closely related to AGB estimations [18]. Moreover, LiDAR could serve as a critical bridge between ground-based measurements and satellite imagery [13; 19]. Sentinel-2 imagery provides data with continuous spatial coverage, which, in combination with LiDAR, enables wide-area AGB estimation. Building on this, the present study developed optimal tree height-DBH growth models for major tree species based on measured individual tree data. Using these models, along with biomass equations, a high-quality AGB sample dataset was constructed by integrating LiDAR data and tree species information. Furthermore,Sentinel-2 data, combined with machine learning models, were utilized to explore the feasibility of AGB estimation in GGP stands.
# Communication Interface Manager for Improving Performance of Heterogeneous UAV Networks
## 6 Obtained Results
### MAC and PHY Evaluation
Observed messages delay related to the use of the IM can be considered acceptable for transmitting 480p video sessions and for sending the GPS coordinates mentioned in Section 6.1, and data and telemetry, where such delays generally do not result in a noticeable decay in experience by a user, for example. However, the delays recorded from 200,000 samples in the experiment with three nodes and five nodes would not be acceptable for voice-over WLAN applications, as connections are typically dropped if the delay exceeds 150 ms [45]. So, depending on the application data to be transmitted over the network, the delays obtained in our experiments are considered tolerable. Figure 13: Aggregated RSSI and noise samples. In general, the IM selected the best interface most of the time due to a proper dynamic analysis of the medium conditions, in accordance with the variations in the distance between the UAVs. For the five- and eight-node scenarios, with increased communication stress due to the larger communication range and amount of data trafficked, although the IM did not produce the best performance in the evaluated metrics overall, it guaranteed more data being transmitted with more-stable, better-quality connections. This is due to its performance in terms of Rx power (Figure 11) and loss (Figure 12). The IM also presented a greater throughput and PDR, and higher volume of packets transmitted, allowing better message flow.
## 3 Linear Mathematical Model of the Bearing Stationary State
### Boundary Value Problems for a Dimensionless Function of Pressure in the Bearing Gap  
When the axes of the shaft (1) and the sleeve (3) are parallel, and the pressure function \(P(Z,\varphi)\) in the bearing gap satisfies the stationary Reynolds equation [25]:  
\[\frac{\partial}{\partial\varphi}\bigg{(}H^{3}P\frac{\partial P}{\partial \varphi}\bigg{)}+\frac{\partial}{\partial Z}\bigg{(}H^{3}P\frac{\partial P}{ \partial Z}\bigg{)}=0, \tag{1}\]  
Figure 1: Design diagram of a radial aerostatic bearing with displacement compensator. (**a**) Longitudinal section, (**b**) suspension unfold drawing.  
where \(Z\) and \(\varphi\) are longitudinal and circumferential coordinates, and  
\[H(\varphi)=1-\varepsilon(\tau){\rm cos}\varphi, \tag{2}\]  
is a function of the thickness of the bearing gap and \(\varepsilon\)--eccentricity.  
To formulate the boundary conditions for (1), we introduced the local coordinate systems. We will consider the symmetrical right half of the bearing. In the inter-row part, \(0\leq Z\leq L_{1}\), and, in the end part, \(0\leq Z\leq L_{2}\); for both of these parts, \(0\leq\varphi\leq 2\pi\), where \(L_{1}\) is half the length of the inter-row part, \(L_{2}=L-L_{1}\) is the length of the end parts, \(L_{3}\) is half the length of the throttle chambers, \(L\) is half the length of the bearing and \(R_{1}\) is the outer radius of the sleeve (3).  
The boundary conditions for half of the inter-row part are  
\[\frac{\partial P}{\partial Z}(0,\varphi)=0,\,P(L_{1},\varphi)=P_{k}(\varphi), \tag{3}\]  
for the end part:  
\[P(0,\varphi)=P_{k}(\varphi),\,P(L_{2},\varphi)=1, \tag{4}\]  
and for both parts:  
\[P(Z,\varphi)=P(Z,\varphi+2\pi),\,\frac{\partial P}{\partial\varphi}(Z,\varphi )=\frac{\partial P}{\partial\varphi}(Z,\varphi+2\pi), \tag{5}\]  
where \(P_{k}(\varphi)\) is the function of pressure on the injection line at the air outlet from the damping feeders.  
The initial condition for (1) is the pressure function \(P_{0}(Z)\) with the coaxial position of the shaft and sleeve at \(\varepsilon\) = 0, no load and no shaft rotation. This function can be found by solving the boundary value problem:  
\[\left\{\begin{array}{c}\frac{d^{2}P_{0}^{2}}{dZ_{2}^{2}}=0,\\ \frac{dP_{0}}{dZ}(0)=0,P_{0}(L_{1})=P_{k0},\\ P_{0}(0)=P_{k0},P_{0}(L_{2})=1,\end{array}\right. \tag{6}\]  
where \(P_{k0}\) is a constant value equal to the value of the pressure function at the air outlet from the annular diaphragms.
## 3 Results and Discussion
### DFT Analysis  
Figure 14 shows the optimized hydrated structure of molybdenite (001) surface, STG adsorption structure on hydrated molybdenite (001) surface; CuOH\({}^{+}\) adsorption structure on hydrated molybdenite (001) surface, and STG adsorption structure on CuOH\({}^{+}\) modified molybdenite (001) surface. The average adsorption energy of H2O was \(-1.42\) kJ/mol, indicating the molybdenite (001) surface was very hydrophobic, which agrees with the previous research [47]. When STG further adsorption on hydrated molybdenite (001) surface, the calculated adsorption energy was \(+264.94\) kJ/mol, which is a positive value, indicating that STG cannot be adsorbed on hydrated molybdenite (001) surface when no Cu ions present, which is consistent with the previous XPS results. After CuOH\({}^{+}\) adsorption, the adsorption energy of CuOH\({}^{+}\) reached \(-814.58\) kJ/mol and a Cu-S bond of Cu in CuOH\({}^{+}\) and S in molybdenite (001) surface with length of \(2.17\) A was generated, indicating Cu ion was very easily to be adsorbed on the hydrated molybdenite (001) surface. And after further adsorption of STG on CuOH\({}^{+}\) modified molybdenite (001) surface, a new Cu-S bond of Cu in CuOH\({}^{+}\) and S in STG with length of \(2.23\) A was produced and the STG adsorption energy was also a negative value of \(-344.37\) kJ/mol, indicating STG adsorption on hydrated molybdenite (001) surface was enhanced when Cu ions present. The above DFT calculation results demonstrated that when copper ions are present in the pulp, STG also interacts with molybdenite, significantly depressing its floatability and making it  
Figure 13: Mo 3d XPS spectra of molybdenite: (**a**) untreated, (**b**) exposed to Cu\({}^{2+}\), (**c**) exposed to STG, and (**d**) exposed to both Cu\({}^{2+}\) and STG.  
more difficult to separation of molybden from chalcopyrite under STG inhibition. These observations are in line with the results from flotation experiments outcomes and all the characterization techniques mentioned above.
## 5 Conclusions  
In this study, a vehicle-mounted emergency communication system using BeiDou RSMC for firefighting operations in forest areas without a public network was designed, which was realized by applying BeiDou RDSS short message communication. In addition, the research also used Zigbee self-organizing network technology to reliably convert the fire parameters into digital signals and transmit them. The performance of the system was analyzed, and its reliability was assessed after field tests. This study makes the following contributions:  
1. By combining BeiDou RDSS short message technology and Zigbee self-organized network technology, a vehicle-mounted emergency communication system using BeiDou RSMC for firefighting operations in forest areas without a public network was designed to realize emergency communication between on-site firefighting operators and a remote command center.
2. Tests on the packet loss rate, LQI, and SNR of the emergency communication system were carried out. The results show that the system has excellent performance and can solve the emergency communication problem in forested areas without a public network, and the reliability of the system meets the requirements of actual firefighting operations.  
3. The designed emergency communication system can be mounted on different firetrucks, and other monitoring parameters can be added according to the actual application scenario; at the same time, it can also be matched with BeiDou communication cards with different frequencies and communication levels according to the specific use requirements.  
Conceptualization, C.X.; methodology, S.S. and Z.D.; software, C.X. and Y.Z.; validation, C.X.; formal analysis, Z.D.; investigation, Z.D.; resources, C.X.; data curation, Y.Z.; writing--original draft preparation, C.X.; writing--review and editing, C.X.; visualization, C.X.; supervision, S.S.; project administration, Y.Z.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the National Key Research and Development Program of China (2018 YFE0207800-04).  
The data in this study are available from the authors upon request.  
The authors thank Northeast Forestry University Experimental Forestry for site support.  
The authors declare no conflicts of interest.