esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulations
### Mathematical Model  
#### 3.2.1 Model Assumption  
It is necessary to introduce some appropriate assumptions to improve the efficiency of numerical computation. The assumptions are as follows [28; 29; 30; 31; 32]:  
1. The properties of the intact rock are assumed to be isotropic, homogeneous, and constant.
2. Since the specific flow process is not concerned within the SRV where a porous medium with homogeneous and isotropic properties is regarded as equivalent.
3. The chemical and mechanical interactions between fluid and rock are ignored.
4. Fluid flow is laminar within porous media and obeys [PERSON]'s law.
5. Local thermal equilibrium is maintained between rock and fluid.
6. Water is adopted as the working fluid.  
The properties changing with temperature with regard to water follow the following formulas [33]:  
\[\mu=\left\{\begin{array}{l}1.379-0.021\cdot T+1.360\cdot 10^{-4}\cdot T^{2}-4.645 \cdot 10^{-7}\cdot T^{3}+8.904\cdot 10^{-10}\cdot T^{4}-9.079\cdot 10^{-13}\cdot T ^{5}+3.845\cdot 10^{-16}\cdot T^{2}273.15\;\mathrm{K}\leq T\leq 413.15\;\mathrm{K}\\ \\ 0.004-2.107\cdot 10^{-5}\cdot T+3.857\cdot 10^{-8}\cdot T^{2}-2.397\cdot 10^{-11} \cdot T^{3}413.15\;\mathrm{K}\leq T\leq 553.75\;\mathrm{K}\end{array}\right. \tag{1}\]  
\[c_{p}=12010.147-80.407\cdot T+0.309\cdot T^{2}-5.381\cdot 10^{-4}\cdot T^{3}+3.6 25\cdot 10^{-7}\cdot T^{4}273.15\;\mathrm{K}\leq T\leq 553.75\;\mathrm{K} \tag{2}\]  
\[\rho=\left\{\begin{array}{l}1000\cdot\left(1-\frac{(T-277.13)^{2}}{503570} \cdot\frac{T+9.85}{T-205.89}\right)273.15\;\mathrm{K}\leq T\leq 293.15\; \mathrm{K}\\ 996.9\cdot\left(1-3.17\cdot 10^{-4}\cdot(T-298.15)-2.56\cdot 10^{-6}\cdot(T-298.15) ^{2}\right)\;\;293.15\;\mathrm{K}\leq T\leq 523.15\;\mathrm{K}\end{array}\right. \tag{3}\]  
where \(\mu\) is viscosity, \(c_{p}\) is the isobaric heat capacity, \(\rho\) is density, and \(T\) is Kelvin temperature.
# Energy-Aware System Design for Autonomous Wireless Sensor Nodes: A Comprehensive Review
## 3 Energy Supply for Wireless Sensor Nodes
### Overview of Energy Converters  
Harvesting from ambient sources is, despite the manifold harvesting principles and technologies available, still challenging due to the limited generated energy and instability of the sources depending on the application and the environmental conditions. To enhance the available energy for WSNs, several studies has been conducted aiming to improve the performance of energy harvesting systems considering real ambient sources [4]. Thereby, each ambient source and converting principle has its own characteristic and power density level. Table 1 presents a summary of the most common existing ambient sources including solar, wind, vibration, thermal, ocean waves, nuclear reaction, acoustic noise and radio frequency (RF) energy. The highest energy income can be realized from solar cells outdoors (see Table 1). This possibility remains the most predictable as weather data can be used as a priori knowledge in energy management [32]. Mechanical energy is also remarkably interesting as it provides possibilities to adapt the harvester to the source and extract a high level of energy [33]. It is also interesting from the point of view of availability, e.g., in environmental and industrial applications. An ambient energy source such as vibration can be found in several applications, such as in industrial field in machines, transport, infrastructure such as bridges and human movements for wearable applications. The wind source [20] is widely available in the environment but the challenges for harvesting energy are often the weak wind, maintenance cost and the strong fluctuation. Further, as source, we can find the ocean wave energy, which is considered as the largest estimated global resource form of ocean energy, and it is useful for several applications such as electricity generation, water desalination and pumping of water [34].Acoustic noise is well known as a source for energy harvesting in industrial plants, transport (e.g., airplanes, vehicles and high-speed trains) and loudspeakers. Thermal energy harvesters [19] have a relatively weak energy income at 40 \(\upmu\)W/cm\({}^{3}\) and are therefore interesting if they can be applied with a big surface or at high temperature differences in certain environments. To supply wireless sensors, the availability of the energy source and its properties are important. They should be considered in the design to maximize the energy income and increase efficiency. The harvester needs to be scaled depending on the expected energy level and the necessary supply level. To compensate energy fluctuation and, especially depending on the length of time intervals, where the energy income is inferior to the energy consumption, an energy storage unit needs to be selected and dimensioned, as explained in Section 4.
# Advanced Hybrid Beamforming Technique in MU-MIMO Systems
## 5 Simulation Results
### Computing Complexity According to the Number of Bits  
Table 3 shows the computing complexity according to the number of bits comparing the conventional and the proposed hybrid beamforming system. The equation of computing complexity is as follows,  
\[28\times k\times B^{2}\times I \tag{12}\]  
Because the rest of the elements are the same, other parameters such as the number of antennas and the size of FFT are not considered. \(I\) is the number of iteration. As simulation parameters, the number of users are 4 and the number of iteration is 10,000. The computational complexity for the conventional and the proposed hybrid beamforming system is same. The only difference between  
Figure 14: The average sum rate of the hybrid beamforming schemes according to the number of transmit antennas, SNR = 20 dB.  
Figure 13: The average sum rate of the hybrid beamforming schemes according to the number of transmit antennas, SNR = 8 dB.  
the conventional and the proposed hybrid beamforming systems are feedback channel. However, the feedback channel at the proposed hybrid beamforming has a little burden.
# A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks
## 6 Conclusions  
In this paper, we design a glider-assisted linkdisruption restoration mechanism for UWANs, which includes a LDR-GS mechanism and GALO. In LDR-GS, when any cluster head node discovers a link disruption among cluster members, the corresponding link state flag in every cluster head node becomes 1. On the other hand, a glider periodically approaches the cluster head node and the corresponding link state flag in the gliders also becomes 1. Then the glider is scheduled to go to the location for restoring a link disruption, but due to the limited trajectory of the underwater glider, we further design GALO to improve the efficiency of the glider-assisted link disruption restoration mechanism. In GALO, we combine the specific sawtooth movement of the glider and the channel attenuation model to select the optimal relay location. This can enable underwater gliders to accurately restore network connectivity as relay nodes of the disrupted links. We evaluate the performance of different parameters on the glider-assisted link disruption restoration mechanism and compare it with OVAR, HHVBF and VBF algorithms. Simulation results show that the communication energy consumption is reduced by about 20-58% compared with other algorithms and the packet delivery rate is also increased about 15-33% because the glider-assisted link disruption restoration mechanism can change the topology of the entire network to a lesser degree during execution. Compared with other algorithms, the glider-assisted link disruption restoration mechanism proposed can flexibly complete restoring disrupted links because it can schedule free-moving gliders for link restoration. For any link, it can complete the restoring task, while other algorithms have limitations for some link disruptions among key nodes. Therefore, the glider-assisted link disruption restoration mechanism is more general for restoring link disruptions and reinforcing the connectivity in UASNs.  
This work was supported by the National Natural Science Foundation of China (61571318), (61701335), Natural Science Foundation of Tianjin (17 JCQNJC01300), and the Guangxi Science and Technology Project AC16380094. This work was modified with the help of [PERSON].  
[PERSON] conceived, designed and performed the experiments; [PERSON] and [PERSON] analyzed the data; and [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Figure 7: The change of communication energy consumption over time.
# Phased Array Antenna Analysis Workflow Applied to Gateways for LEO Satellite Communications
## 4 Patch Antenna Design
### Design of Transmission Patch Antenna  
The frequency band of the transmission antenna is centred at 28.75 GHz and ranges from 27.5 GHz to 30 GHz. Figure 6 shows the ADS substrate model of the six-layer Printed Circuit Board (PCB) used for electromagnetic (EM) simulations. The top layer (COND2) is used for the patch antenna, and the bottom layer (COND) is used for the beamforming circuitry. PC1 and PC2 are the ground planes of the top and bottom layers, respectively, and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Uplink Antenna**} & \multicolumn{4}{c}{**Downlink Antenna**} \\ \cline{2-9}
**Satellite** & **SpaceX** & **Telesat** & **OneWeb** & **Amazon** & **SpaceX** & **Telesat** & **OneWeb** & **Amazon** \\ \hline Diameter (m) & 1.5 & 1.5 & 3.5 & 2.4 & 1.5 & 1.5 & 3.5 & 2.4 \\ \hline Above ground & 1.95 & 3.2 & 4.55 & - & 1.95 & 3.2 & 4.55 & - \\ \hline EIRP (dBW) & 68.4 & 75.9 & 63.2 & 62.3 & 39.44 & 38 & 30.6–39 & 36 \\ \hline Max Gain & 41 & 31.8 & - & 53.1 & 46.9 & 45.5 & 51.5 & 50.2 \\ \hline Elevation & 25 & 10 & 15 & 20 & 25 & 10 & 15 & 20 \\ \hline Path distance (km) & 2439 & 1504 & 1684 & 590 & 2439 & 1504 & 1684 & 590 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Gateway antennas.  
the dielectric between COND2-PC1 and COND-PC2 is a Taconic TLX-8 fibreglass substrate with 2.55 relative permittivity (\(\varepsilon_{r}\)) and 0.79 mm height. PC1 and PC2 are glued back-to-back with a 0.1143 mm thickness TPG-32 pre-impregnated material. PCVIA1 and PCVIA2 are the vias to connect the conductor (COND2 and COND) layers to their respective ground planes (PC1 and PC2). A third via (HOLE) is needed to connect the antenna with the beamforming circuitry [37].  
Using (4)-(9), the patch antenna dimensions were calculated. The results are summarized in Figure 7. The width and length of the antenna are 3.9 mm and 2.5 mm, respectively, and the feeding point for a 50 \(\Omega\) input impedance is located at \(X_{f}=1.6\) mm and \(Y_{f}=2\) mm.  
The simulated reflection coefficient (\(S_{11}\)) is shown in Figure 8a. The reflection coefficient is better than \(-10\)dB over the whole frequency band (from 27.5 GHz to 30 GHz) and its minimum is located at the centre of the band. As shown in Figure 8b,c, the maximum gain is 7.5 dB.  
Figure 5: Impedance distribution in a patch antenna [36].  
Figure 6: ADS model of the selected substrate for the transmission antenna composed of two TLX-8 substrates glued back-to-back with TPG-32 pre-impregnated material.  
Figure 7: Transmission antenna layout diagram.
# A GNSS/5G Integrated Three-Dimensional Positioning Scheme Based on D2D Communication
## 2 Methods
### Positioning Model in 5g  
We assume that the BSs and the MUs are all equipped with GNSS chips, indicating that the time between them is synchronized. In the three-dimensional plane, there are two positioning methods for a requesting node: (_i_) the positioning signals from the GNSS satellites can be directly calculated to obtain the position, and (_ii_) the TOA and RSS measurements of four reference nodes (the BSs and the cooperative nodes), which can be transformed into the distances, are enough to be utilized for positioning if the MUs' time offset is ignored. In practice, TOA-based positioning and RSS-based positioning are very popular due to their ready availability. To utilize the advantages of TOA and RSS measurements and improve the positioning accuracy, we exploited both of them to estimate the distances between the requesting nodes and the reference nodes, referred to as the joint TOA/RSS method.  
In essence, the GNSS positioning and TOA- and RSS-based positioning supplemented with each other, which can be integrated to obtain a more accurate solution, and we illustrate the integrated method in the next subsection. Here, we mainly focus on the distance estimation based on TOA and RSS measurements. In the following, the distance-based positioning is described firstly, and then, TOA and RSS measurements are analyzed in detail to estimate the distances between the nodes, respectively.
# Numerical Phase-Field Model Validation for Dissolution of Minerals
## 4 Problem Description and Model Tests
### Simulation Case Study Based on Available Measured Experimental Data of Mineral Particle Dissolution  
Most of the available experimental data on solid dissolution in the literature focus on recording the evolution of solute concentration [11; 14; 80; 81] or dissolved solid mass over time, and do not explicitly observe the movement of the solid-liquid boundary [80; 81]. In some experiments, the dissolution process was influenced by convection with stirring [82; 83; 84; 85]. In this sense, sodium chloride (NaCl) is one of the most common minerals: its congruent dissolution mechanism and the corresponding reaction thermodynamics and kinetics are well documented in the literature [74; 86; 87; 88; 89; 90; 91]. However, studies addressing solid-liquid boundary regression due to diffusion-controlled dissolution have mainly tended to focus on the nanoscale [92; 93; 94].  
The study of [PERSON] and [PERSON] [95] is one of the few studies that provides detailed experimental dataset to be used for the experimental validation of the PF numerical models. They performed image analysis by coupling a digital camera to a stereo microscope to obtain microscopic images of the dissolution process. Recording started by placing a single NaCl particle in 500 \(\mu\)L of water without stirring at 20 \({}^{\circ}\)C. The time profile of dissolution was obtained by calculating the projected area of the single crystal as a function of dissolution time from the video microscope image.  
Based on these experimental data, one-dimensional simulations of the dissolution process of a single salt particle performed, in a spherical coordinate system, and two-dimensional simulations of the dissolution process of three different shapes of NaCl particles, namely round, ellipsoidal and irregular, have been performed by using the PF model. Neumann no-flux boundary conditions are applied for 1D simulation. Periodic boundary conditions are used for 2D simulations. The thin interface limit is supported by the chosen experimental case of the highly soluble mineral crystals, which can be considered as non-porous. This results in negligible solid-liquid thickness (at the mesoscopic scale) and it has been considered in this paper. The employed parameters are summarized in Table 2. For easier implementation of energy equations at microscale, all length dimensions were normalized by the solution radius and energy terms normalized by \(A\) (see Appendix B).
## 2 Bimodal Information Collection System
### UAV Carrying System and Ground Workstation  
The overall architecture of the bimodal information collection system is shown in Figure 1. The M100 Quad-rotor UAV system [30], which could vertical take-off and land with a payload of 1245 g, is adopted here to carry the multispectral camera and the bio-radar module. The ground workstation is responsible for information transmission, target identification, and system control. The coordination between the system mainly includes four parts: (1) the MSS module on the cruising UAV first detect the suspected targets; (2) ground workstation sends commands to the UAV, which automatically triggers an airborne dropper to drop the bio-radar to a location around the target; (3) the bio-radar sensor can transmit asymmetric wide beam signal and obtain the microwave echo reflected by human chest moving. After being filtered and amplified, the respiratory signal is converted into digital signals through STM32 A/D and sent to the ground -workstation receiver by LoRa module; (4) by further analysis and judgment based on embedded algorithms, both respiration rate detection and human target property re-identification could be realized.
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 4 Proposed Push-Based Data Forwarding Scheme with Fuzzy Logic
### Selection of Cluster Head (CH) using Fuzzy Logic
#### 4.2.3 Direction (Towards Producer)  
In this step, the direction of the node/vehicle is obtained by taking the slope. If the slope is zero degree, the node/vehicle is assumed to be moving toward the producer of the critical data packet. If the slope is \(180^{\circ}\), the node/vehicle is believed to be moving in the opposite direction. In this case, the node/vehicle will not be entertained for the selection of the CH due to its direction. Equation (7) is used for the vehicle's direction.  
\[slope_{vi}=\begin{cases}1,&\text{if }slope_{vi}=0\\ 0,&\text{if }slope_{vi}=180\end{cases} \tag{7}\]
## 3 Case Study  
In this paper, MATLAB is used as the operating environment, and the model proposed in this paper is validated by using the data in 2013 of certain photovoltaic power stations in Yunnan Province of China and certain photovoltaic power station in Gansu Province in China, two provinces that have a large difference between latitudes and altitudes. Both Yunnan and Gansu photovoltaic power stations are equipped with monocrystalline silicon photovoltaic cells. The data of Yunnan photovoltaic power station is an instantaneous value per 15 min, with 96 data points per day. The data of Gansu photovoltaic power station is an instantaneous value every 10 min, and there are 144 data points in a day. The data includes the photovoltaic output, time, temperature and irradiance of the power station. In the prediction process, data, from 2 January 2013 to 23 December 2013, was used to establish the model, and then conducted the prediction on the data from 24 December 2013 to 30 December 2013. In addition, other data such as time, temperature, etc. in the data are used to establish the feature matrix \(F\).
## 3 Plasmonic Optical Fiber Aptasensors  
The optical fiber biosensors represent a highly sensitive class of optical biosensors. The sensing principle used in silica and polymer fiber probes can be based on different approaches: intensity measurements (through tapered fibers), gratings, SPR or LSPR, evanescent field, etc., [1; 38; 39; 40; 41]. These methods can be implemented by two main sensor configurations, reflection or transmission mode, depending on the position of the light source and the detector with respect to the sensing point. Usually, in reflection mode, the sensing region (for instance, the tip of the fiber) is located away from the equipment (source and the detector); whereas, in transmission mode, the sensing region is typically located in the middle of the fiber, between the source and the detector. In particular, among the different sensor configurations, the plasmonic one used in transmission mode can be simply realized by removing the cladding around the fiber and depositing a thin metallic film on the exposed core. Then, working on the refractive index of the core, the shape of the sensing region, the fiber's type, and other structural parameters, it is possible to excite the plasmonic phenomena by achieving different performances. Moreover, the plasmonic sensor's performance can be improved using tapered fibers or metal nanostructures [42] or by changing the physical shape, such as in the U-bend configuration [43]. This high sensitivity of fiber-based SPR/LSPR sensors suggests the possible employment in the development of biosensors. In this context, it should be noted that the sensing principles and the possible configurations of optical fiber (OF) biosensors were recently summarized by [PERSON] et al. in [41]. During the last decade, several examples of OF-based biosensors can be found in the literature. In fact, different molecular recognition elements have been successfully immobilized on OF-based probes, such as antibodies, MIPs, and aptamers, proving the high versatility of these simple and low-cost platforms. More specifically, Table 2 collects the published works coupling OF-based plasmonic platforms (both silica and POFs) and aptamers. A first example of the coupling between a fiber-based SPR probe and an aptamer was reported by [PERSON] et al. in 2009 [44] by testing a silica-based fiber covered by a functionalized gold nanofilm in the detection of DNA hybridization and also in the detection of IgE through an aptamer. Since then, several examples have been reported, mainly based on silica fibers with applications in cancer biomarkers detection [45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63], toxins [64; 65; 66; 67], allergens [68; 69; 70], small molecule or DNA [62], and virus [71], as summarized in Table 2. The highest sensitivity was achieved using nanostructures, such as nanoantennas [50; 65], nanorods [64], or Bragg grating nanoimprinted on the fibers [55]. Moreover, these fiber-based optical devices can be miniaturized and easily integrated into compact platforms [63; 70].