esa-sceva/satcom-chunk-collection huggingface dataset:
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 4 EMF Exposure Level and 5G Technologies
### EMF Evaluation Methodology
#### 4.2.2 EMF Exposure Evaluation Metrics  
In mobile communication systems, EMF exposure is evaluated differently for downlink and uplink transmission. Downlink measurements refer to far-field radiation exposure, where the EMF radiation from the base station's (BS) transmitting antenna is measured at a distance far from the source. In contrast, uplink measurements involve near-field radiation exposure, occurring when a user's mobile device transmits data to a BS. In this case, the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
radiated energy is absorbed primarily by the user's body or head due to the proximity of the device.  
The primary metrics used for EMF exposure evaluation are power density (PD) for downlink transmissions and specific absorption rate (SAR) for uplink transmissions.
* **Power density (PD):** PD quantifies the EMF exposure from downlink transmission and is expressed in watts per square meter (W/m\({}^{2}\)). The power received by an antenna at the receiving end is given by [90] \[\mathrm{P_{R}}=\mathrm{P_{D}A_{e}},\] (1) where \(\mathrm{A_{e}}\) is the effective area (or aperture) of the receiving antenna and \(P_{D}\) is the power density (PD) of the radiated signal. For an isotropic antenna, the effective area is calculated as \[\mathrm{A_{e}}=\frac{\mathrm{G}\lambda^{2}}{4\pi},\] (2) where \(\lambda\) is the wavelength of the signal and \(\mathrm{G}\) is the antenna gain. Thus, PD can be derived as follows: \[\mathrm{PD}=\frac{\mathrm{P_{R}}}{\mathrm{A_{e}}}.\] (3)
* **Specific absorption rate (SAR):** SAR measures the rate at which the human body absorbs electromagnetic energy from a nearby transmitting antenna. It is expressed in watts per kilogram (W/kg) and is a critical metric used by regulatory bodies to set exposure limits and compare the safety of mobile devices [91]. Mathematically, the SAR at a point \(P\) is defined as [92; 93] \[\mathrm{SAR}(P)=\frac{\sigma|\mathrm{E}(P)|^{2}}{\rho},\] (4) where \(\sigma\) is the conductivity of the tissue (S/m), \(\mathrm{E}(P)\) is the electric field intensity (V/m), and \(\rho\) is the mass density of the tissue (kg/m\({}^{3}\)).  
Table 4 provides an overview of the current exposure limit recommendations for PD and SAR as established by various international regulatory bodies. These standards serve as guidelines to ensure safety and compliance in wireless communication systems.
## 5 Simulation
### Increasing Number of Connections
#### 5.1.3 Route Discovery Frequency and Routing Overhead  
In this case we calculated the average route discovery frequency of the three protocols per second. This is the number of times a protocol has to discover paths to the destination. The results shown here are intra-cluster-based discovery and does not include hotline assisted gateways, _i.e._, The source and destination nodes are in same group of nodes. As shown in Figure 12, AOMDV has the minimum number of route discovery requests, and the reason for this is because AOMDV discovers all mutipaths available from source to destination, so when a path fails it does not have to rediscover, it simply uses the next available path. The disadvantage of this approach is overhead in route discovery process as it takes more time to discover all the paths available.  
WEAMR resolves this issue by limiting the discoverable multipath value to two paths maximum. As discussed in [28], not all the paths discovered are suitable for routing and only minimum hop count paths contribute much to performance. Figure 12 shows result of route discovery frequency where WEAMR performs better than AODV. Figure 13 shows results for routing overhead of overall communication, in this simulation WEAMR shows significant less overhead due to less number of nodes involved in routing as hotline assisted routing saves a lot of hops in the transmission.  
Figure 12: Average Route Discovery frequency with increasing number of connections.  
Figure 13: Average Routing overhead with increasing number of connections.
# State of Charge Estimation for Lithium-Ion Power Battery Based on H-Infinity Filter Algorithm
## 2 Establishment of Fractional-Order Model and Parameter Identification
### Lithium-Ion Battery Test Experiment  
Battery test experiments are the premise of battery model establishment and state estimation, and are an indispensable step in battery research. In this paper, the battery test system is shown in Figure 2. The test object is a universal A123, ternary lithium-ion soft packet battery cell. The main parameters are listed in Table 1.  
In order to identify the model parameters, a series of experiments on lithium-ion batteries were conducted with reference to relevant national standards and the USABC Electric Vehicle Battery Test Procedures Manual. The battery test flow chart of the relevant experiments is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nominal Capacity (Ah)** & **Nominal Voltage (V)** & **Charging Cut-Off Voltage (V)** & **Discharge Cut-Off Voltage (V)** & **Charging Cut-Off Current (A)** \\ \hline
28 & 3.7 & 4.2 & 2.5 & 1.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main performance parameters of universal A123 battery.  
Figure 3: Battery test flow chart.  
Figure 2: Software and hardware system for battery test experiment.  
According to the experimental data obtained by the double-pulse test, the relationship curve between OCV and SOC can be obtained by [33] using the empirical Equation (8), as shown in Figure 4.  
\[U_{OCV}(SOC)=C_{0}+C_{1}SOC+C_{2}\frac{1}{SOC}+C_{3}\ln(SOC)+C_{4}\ln(1-SOC) \tag{8}\]
###### Abstract  
This paper presents a preliminary proof-of-concept study of a novel approach to 3D millimeter-wave (MMW) imaging, demonstrating the first implementation of Glow Discharge Detectors (GDDs) in this domain. GDDs offer significant advantages over conventional MMW detectors like Schottky diodes or bolometers due to their cost-effectiveness, robustness to high-power MMW signals, and reliable operation under diverse environmental conditions. Based on weakly ionized plasma (WIP) technology, GDDs detect changes in discharge current upon MMW exposure, providing an affordable and durable alternative to traditional MMW imaging systems. The system operates within a subset of the W-band (101-109 GHz), utilizing a customized transmitter (Tx 272 from VDI Technologies), which operates at a frequency range proportional to the VCO supply voltage level. The Frequency-Modulated Continuous Wave (FMCW) signal source is split into target and reference paths via a compact waveguide splitter, improving stability and reducing the complexity of the optical setup. Reflected signals are processed by the GDD, which functions as a heterodyne receiver, and Fast Fourier Transform (FFT) is used to extract range data. A 2D grid scanning mechanism, controlled by step motors, maps the surface of the object, while depth information is derived from FMCW frequency differentials to construct a complete 3D profile. This work demonstrates the potential of GDD-based 3D MMW imaging as a low-cost, efficient solution for security screening and industrial inspection. By addressing challenges in cost, scalability, and performance under high-power MMW signals, this approach represents a significant step forward in making MMW imaging technology more accessible, while highlighting the need for further development to achieve practical implementation.  
m +
Footnote †: journal: Applied sciences  
_Article_
## 3 Engineering Application
### Comparison of Results  
Figure 8 shows the vertical deformation of the pipeline obtained by different models through calculation. Compared with the traditional Winkler foundation beam model and Pasternak foundation beam model, the method developed in this paper can more intuitively respond to the deformation law between the pipelines and soil, which makes it more suitable for the pre-preparation of shield tunnel construction design. From the figure,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Soil Layer** & **Thickness/m** & \begin{tabular}{c} **Elastic** \\ **Modulus/MPa** \\ \end{tabular} & **Poisson Ratio** & **Gravity/kN- m\({}^{-2}\)** & \
\begin{tabular}{c} **Force of** \\ **Cohesion/kPa** \\ \end{tabular} &
\begin{tabular}{c} **Angle of Internal** \\ **Friction/\({}^{n}\)** \\ \end{tabular} \\ \hline Landfill & 2.1 & 3.0 & 0.42 & 18.0 & 5.0 & 20.0 \\ Silt clay & 7.8 & 4.3 & 0.43 & 16.9 & 11.6 & 23.9 \\ Silt clay & 16.1 & 14.12 & 0.34 & 19.0 & 20.6 & 26.8 \\ Clay & 24.0 & 14.22 & 0.34 & 19.6 & 33.5 & 20.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of soil layers.  
Figure 6: Location of the tunnel study area.  
Figure 7: Stratigraphic section and location of the tunnel (unit: m).  
it can be seen that the method used in this paper is more conservative than the traditional Winkler foundation beam model and Pasternak foundation beam model in predicting the results of pipeline deformation caused by shield excavation; it can be more convenient to predict the vertical deformation of the pipeline under the premise of guaranteeing safety.
## 2 Data and Methodology
### Data  
#### 2.1.1 Anemometer Data  
The measurements were taken at the roof of the University of the Basque Country's building in Eibar, located at the following coordinates: 43\({}^{\circ}\)10\({}^{\prime}\)45.82\({}^{\prime\prime}\) N, 2\({}^{\circ}\)29\({}^{\prime}\)19.80\({}^{\prime\prime}\) W. A cup and vane anemometer was used. It measured the wind speed in intervals of approximately 7 min and uploaded the mean wind speed and the direction of each interval to a web page. The data could later be retrieved using web scrapping techniques. A year of data was used, covering the period from the 22 nd of May 2018 to the same day of 2019. This way, the effect of the changing weather during the year could be taken into account, without having an excessively long measurement period that would make the method unattractive for commercial use.  
Ideally, if one of the facades of the building was already selected, the anemometer would have been installed on the edge between that facade and the roof, where a turbine would be installed. Alternatively, it could be installed on the center of the roof, which would allow to select the facade taking the measurements into account. However, due to architectonic and legal limitations, none of these options were possible, and the anemometer had to be installed on one side of the roof several meters away from the edge.  
The anemometer's data was filtered to ensure that all measurements used in the calibration were taken while the anemometer was working properly. These filters detected suspicious measurements but did not automatically delete them. Instead, the positions of those measurements were stored, so they could be plotted later, along with all the data. That way, the proper functioning of the filters could be checked, and their parameters could be tuned, before deleting any data. Three of them were used:  
Figure 1: Diagram that summarizes the methodology and data used.  
1. The first filter detected measurements with negative wind speed or values greater than 30 m/s.
2. The second one detected if the difference between two subsequent measurements was greater than 5 m/s.
3. The third one detected whether 20 subsequent measurements varied their mean wind speed less than 0.05 m/s.  
When applying these filters, the first one did not detect any errors, the second one detected 3, and the third one detected 117. Overall, a 0.12% of the data points were flagged as erroneous and deleted, so the filters did not meaningfully affect the data set.
## 2 Positioning in the Forest
### Global Navigation Satellite Systems (GNSS)
#### 2.1.3 Emerging Systems: Galileo and BeiDou  
The European satellite navigation system, Galileo, is intended for civilian use and it has been under development for almost a decade. Problems in financing, design, and interoperability (with, for example, the US GPS) have delayed the plans for and deployment of the new system. One of the foremost motives for developing Galileo has been the goal of making it independent from the military-operated systems. Dependency on the other systems is considered to adversely impact European security and to bring about too much insecurity for all of the applications and services currently depending on satellite-based navigation. Two test satellites have already been in orbital operation since 2005 and 2008, and in October 2011 two of the first in-orbit validation satellites were launched successfully to their planned orbits, and a year later two more were launched. The Galileo system will consist of a total of 30 satellites orbiting at an altitude of about 23,000 km [23]. Galileo is expected to be fully operational around 2018-2020, and Galileo satellites will be transmitting signals on the same frequencies as GPS, but modulated using different code techniques [20].  
The Chinese satellite navigation system under development, _i.e._, BeiDou (COMPASS) Navigation Satellite System, is somewhat similar in design to the US GPS. BeiDou will consist of 35 satellites, five of which will orbit in geostationary orbits. The geostationary part will transmit differential error corrections related to GNSS signals. BeiDou is estimated to be operational in 2020 at the latest [20].
# Modeling of Satellite-to-Underwater Integrated FSO-PON System Using NOMA-VLC
## 5 Conclusions  
A hybrid TWDM-WDM-PON incorporating OCDMA employing NOMA-VLC is incorporated to offer a faithful FSO-fiber-VLC link for satellite-to-undersea under the impact of different water types in oceans, fiber impairments and noise. It is concluded that the design model offers 10 km FSO, 100 km fiber and 5 m VLC range successfully with 10 dBm SNR for 290 connected devices. In addition to this, OSNR and received power of 39 dB and \(-\)9 dBm, respectively, are obtained at a minimum BER limit of \(10^{-9}\). Moreover, uplink transmission outperforms the downlink with an optimum performance less than 300 field of view. The best achieved receiver sensitivity, splitter power budget, gain and noise figure values are \(-\)32 dB, 10 dB, \(-\)1 dB and 1 dB, respectively. Moreover, compared with existing works regarding the codes and system design, the proposed system design depicts superior performance.  
Conceptualization, V.A., M.K. and H.M.R.A.-K.; methodology, V.A., M.K., H.M.R.A.-K. and S.A.A.; software, V.A., M.K., H.M.R.A.-K. and S.A.A.; validation, V.A., M.K., H.M.R.A.-K. and S.A.A.; formal analysis, V.A., H.M.R.A.-K. and S.A.A.; investigation, V.A. and M.K.; resources, V.A., H.M.R.A.-K. and S.A.A.; writing--original draft preparation, V.A., M.K. and H.M.R.A.-K.; writing--review and editing, V.A., M.K. and H.M.R.A.-K.; visualization, M.K., H.M.R.A.-K. and S.A.A.; supervision, S.A.A.; project administration, H.M.R.A.-K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** All data are available in this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Wavelength** & **G** & **NF** & **P\({}_{1}\)** & **OSNR\({}_{i}\)** & **P\({}_{o}\)** & **OSNR\({}_{o}\)** \\
**(nm)** & **(dB)** & **(dB)** & **(dB)** & **(dB)** & **(dB)** & **(dB)** \\ \hline
1596 (DN) & \(-\)2.5 & 3 & 7 & 109 & 6 & 106 \\
1527.2 (UP) & \(-\)2 & 2.5 & 7 & 109 & 6 & 106 \\
1575 (DN) & \(-\)3 & 3 & 7.5 & 109 & 5.5 & 106 \\
1260 (UP) & \(-\)3.5 & 2.5 & 8 & 110 & 6.5 & 107 \\
1550 (DN) & \(-\)1.5 & 1.5 & 6 & 105 & 5 & 103 \\
1550 (UP) & \(-\)1 & 1 & 6 & 105 & 5 & 103 \\ Red (DN) & \(-\)3 & 3 & 3 & 98 & \(-\)5 & 91 \\ Green (DN) & \(-\)2.5 & 3 & 5 & 103 & \(-\)3 & 101 \\ Blue (DN) & \(-\)2 & 2.5 & 4.5 & 112 & 3 & 106 \\ Red (UP) & \(-\)3 & 3 & 6 & 101 & 2.5 & 100 \\ Green (UP) & \(-\)3 & 2.5 & 4.5 & 108 & 3 & 105 \\ Blue (UP) & \(-\)2 & 2 & 5 & 110 & 3.5 & 107 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Measured performance of hybrid FSO-PON system using NOMA-VLC over 10 km fiber + 10 km FSO + 10 m VLC range for 300 devices in DS and UP links rate in the open ocean.
## 3 Proposed Scheduling Scheme
### Probabilistic Balancing  
This method balances the service with the appropriate end-user by determining whether scheduling is carried out appropriately. This constraint is achieved by examining the tree-based computation that deploys either a probability factor or is used to estimate the detection. Balancing is conducted for the service distributed to the appropriate user, and the validity of the devices is monitored. The access forwarded to the appropriate user is defined in this category and examined in the equation below. Here, queuing is performed to balance the resources. \[\rho=\frac{q_{0}[(S+r_{0})\times(N-M)]}{r_{n}+v_{n}} \tag{4}\]  
In this case, insertion and deletion are performed to balance the resource allocation method; here, it deploys better service detection in the SDN. Here, probabilistic balancing is examined for reliable computation in the tree-based process, and the root node represents the service from the power management. The left-side node indicates power allocation, whereas the right node represents sharing. Based on this, probabilities is performed by evaluating the better detection of congestion in the SDN. Power management distinguishes service handling, and balancing is examined in this process. The examination process indicates cumulative congestion in the network and devices and estimates better power management. Figure 3 presents the queuing process of the CS3. Power management is responsible for transmitting power to the appropriate end-user by deploying power allocation and service distribution. Power allocation is conducted for dynamic user densities and estimates congestion to avoid further failure. Here, scheduling is performed for the power allocation in this probabilistic balancing based on the tree structure, which indicates the point-of-contact-based infrastructure. Thus, probabilistic balancing is conducted for data forwarding in the network from the user to the devices, and it is termed as \(\rho\). The following part discusses recurrent learning for power allocation in the management system. The probabilistic balancing function uses a tree-based methodology to control service distribution and power allocation in the SDN, as shown in Algorithm 2. First, it creates a tree structure, with the root node representing power management services, the left-side representing power distribution, and the right-side branch representing service sharing. The function iterates through the tree for every service request in the queue, sharing or allocatingpower according to the nodes it encounters. Throughout this process, it assesses congestion detection to modify sharing and allocations. SDN systems ensure balanced resource use and effective network functioning by appropriately allocating power and other services to devices.
## 2 Results and Discussion
### Porosity and Pore Diameter  
Porosity and average pore diameter (\(d_{p}\)) values as a function of global conversion are shown in Figures 6 and 7, respectively.  
Figure 6: Evolution of nanoparticles porosity with global conversion in polymerizations at 0.05 (**a**) and 0.15 (**b**) g/min in organic phase dosing rate. Runs M (\(\blacklozenge\)); runs MR (\(\blacktriangle\)).  
Figure 7: Change of pore diameter in nanoparticles with global conversion in polymerizations at 0.05 (**a**) and 0.15 (**b**) g/min in organic phase dosing rate. Runs M (\(\blacklozenge\)); runs MR (\(\blacktriangle\)).  
Irrespective of the dosing rate both variables show a continued increase with conversion, with slightly larger values in porosity (_ca._ 0.5 cm\({}^{3}\)/g) and \(d_{p}\) (8 nm) for the polymerization at higher dosing rate. The relatively low porosity and pore diameters values obtained in this study were similar to those obtained in our previous work in which similar polymerization conditions were employed [13]. On the other hand, the porosity increase with conversion could be explained as a result of the increase in the gel percentage also as the polymerization evolves. This would cause that toluene not be absorbed by the cross-linked chains, which would lead to a phase separation (macrosyneresis) with the continuous phase composed of toluene, unreacted monomers and the dissolved polymer [23]. Furthermore, as the cross-linking increases the chains form compact nanospheres whose number rises with global conversion, resulting in an increment in the number of voids between nanospheres and finally, in an increase in the porosity after toluene evaporation.
## 3 Deep-Learning-Based Channel Estimation Methods
### Model-Driven Deep-Learning-Based Channel Estimation Methods
#### 3.4.3 Model-Driven Channel Estimation Combining AMP Algorithm
[71] & Dual CNN (SCNN + ADCNN) & LS rough channel estimation result & Accurate channel estimation result & LABMSE, problem LMMSE (BLMSMS) algorithms, SFCNN,  
According to the discussion in this section, we can learn that most existing studies on channel estimation using deep learning mainly concentrate on the data-driven approach. However, both data-driven and model-driven approaches have the potential to achieve or even exceed conventional methods' performance in the appropriate application scenarios. Table 5 provides a detailed comparison of the advantages and disadvantages of these two approaches. Nevertheless, the studies mentioned above are limited to traditional communication scenarios and do not address future communication scenarios. As one of the critical techniques for 6G networks, the research on RIS is in full swing. The introduction of RIS improves the performance of communication systems but also increases their complexity, making conventional techniques, including channel estimation, confront new challenges. With the accelerated advancement in deep learning, numerous researchers have adopted this technique to solve the aforementioned issue. The following section will introduce channel estimation methods based on deep learning for RIS-aided wireless communication systems.