esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This paper proposes a prediction model for the DPRS thermal deformation based on the block thermal network method and a prediction model for the DPRS wear under ambient temperature change, in combination with the Archard wear model. The wear law of a DPRS under thermal deformation conditions was studied, and an experimental verification was conducted. The remainder of this paper is organized as follows. Section 2 describes the analysis of the temperature rise of a DPRS at different ambient temperatures and the calculation of the thermal deformation using the block thermal network method, based on the rough surface of the DPRS characterized by the MB function, the actual contact area, and multiscale contact mechanics. A wear prediction model that combines the load distribution and motion characteristics is presented. Section 3 describes the analysis of the temperature rise of the DPRS under ambient temperature changes, the amount of thermal deformation, and the influence of thermal deformation on the load distribution and depth of thread wear. Section 4 presents the validation of the temperature rise and wear models using experimental data. The main contributions and conclusions are presented in Section 5.
# Multilayered Network Model for Mobile Network Infrastructure Disruption
## 6 Simulations and Results  
Based on the theoretical analysis of the MNM concept, simulations in the Matlab environment were designed to simulate the behavior and forwarding of urgent data within the model. The aim of these simulations is to point out the fact that in case of disruption scenario of the fixed infrastructure of 5G and IoT networks, the proposed concept of MNM can take over the role of critical applications and services. Simulations point out that the interconnection of several networks into a hierarchically composed multilayer model has advantages over the use of purely wireless sensor networks in the form of transmission of critical data to functional access points with higher transmission speed and lower delay. The exact description of the simulation scenarios will be provided in following subsections.  
Simulations in Matlab do not account rerouting, network reconfiguration and do not use a lot of network parameters like other simulators such as Ns-3 or OPNET Modeler. However, Matlab enables us to simplify the concept of MNM network and provide proof of concept. With Matlab, it is also possible to use a variety of different mobility models for MANET networks such as Random-Way Point or social-based mobility model described in [43]. Matlab also provides possibilities to implement computationally intensive algorithms such as PSO for clustering. In future, we planed to merge Matlab implemented algorithms with OPNET Modeler to further extend simulations with all network parameters.
## 2 Related Work  
Cooperative communication has been proposed to improve the performance of mobile communication networks. The system performance of MIMO AF cooperative networks over the shadowed-Rician fading model was investigated in [22]. The OP performance of mobile cooperative networks was investigated in [23]. The Stackelberg game was exploited for a hybrid satellite-terrestrial cooperative network, and the harmonic mean function was used to select the best relay node in [24]. The error performance of the spatial modulation system over spatially correlated Rayleigh channels was presented in [25].  
TAS is widely employed in cooperative networks to reduce hardware complexity. In [26], a novel TAS strategy was proposed for full-duplex AF relaying over the Rayleigh model. To reduce multiuser interference, the TAS mechanism was introduced into constant envelope pre-coding [27]. In [28], a TAS-maximal ratio combining relay system investigated the effect of channel estimation error. In [29], to reduce hardware complexity, two TAS schemes were proposed for secure transmission using precoding-aided spatial modulation.  
Traditionally, performance evaluation is achieved by mathematical superposition, approximation, and fitting. These methods are faced with oversimplified real-world issues. Machine learning techniques have overcome these issues and are widely used in performance prediction [30]. In [31], a LWLR method was proposed to predict the damping ratio of a dominant mode online.SVM regression model was used to propose localizing algorithms for large-scale wireless sensor networks in [32]. A novel evolutionary algorithm was proposed for data classification problem with ELM in [33]. Because of good nonlinear prediction ability, a BP neural network model is more suitable for performance prediction. By using a BP neural network, Ref. [34] proposed a monitoring method of total seed mass. In [35], the BP network was used to obtain a direct readout of the applied force. To predict the telecommunication customer churn, Ref. [36] used a particle classification method to optimize the BP network. The authors of Ref. [37] employed BP network for a high accuracy channel estimation in secure cooperative transmission. In [38], the BP network was used to predict the Rayleigh fading channel.
## 3 Estimation of Minimum Achievable Spot Size  
Since the setup uses Fourier holograms, the achievable resolution is mainly determined by the numerical aperture \(NA\) of the microscope objective used. For a Gaussian beam, the theoretical limitations can be derived from the beam parameter product as the lateral spatial resolution \(\Delta x\) of the laser beam defined by the \(e^{-2}\) width of a Gaussian beam and the axial resolution \(\Delta z\) as the full width at half maximum (FWHM) along the optical axis of the beam. Assuming a Gaussian beam with a \(3\sigma\) radius \(r_{3\sigma}\) of  
\[r_{3\sigma}=f\cdot NA \tag{4}\]  
Figure 2: Schematic of the reconstruction of Fourier holograms. The hologram on the SLM is Fourier-transformed with the Fourier lens and then scaled onto the sample using the tube and objective lenses. The calculation of the hologram for the illumination of a point \(P_{xy^{\prime}}\) in the sample plane is executed by first scaling this to the Fourier plane using Equations (2) and (3) followed by a Fourier transform.  
being focused by a lens with the numerical aperture \(NA\) and focal length \(f\) to not apodize the Gaussian profile too severely, the minimum achievable spatial resolution is then  
\[\Delta x_{Gauss}=M^{2}\frac{\lambda}{NA} \tag{5}\]  
\[\Delta z_{Gauss}=\ \frac{3}{2}M^{2}\frac{\lambda}{NA^{2}} \tag{6}\]  
with the beam quality parameter \(M^{2}\). Due to the overfilling of the microscope objective, the resulting shape of the focus in the sample plane is an Airy disc whose diameter is usually defined by the first minimum of the distribution,  
\[\Delta x_{Airy}=1.22\frac{\lambda}{NA} \tag{7}\]  
For comparison with the Gaussian distribution, the \(e^{-2}\)-width of the central lobe of an Airy disc equals  
\[\Delta x_{Airy,e^{-2}}=0.82\frac{\lambda}{NA}. \tag{8}\]  
Since laser beams in general are characterized by their \(D_{4\sigma}\)-width, we simulated the Airy patterns resulting from the diffraction of lenses with different numerical apertures. We find that the general relation for the diameter of Airy discs is  
\[\Delta x_{Airy,D_{4\sigma}}=1.14\frac{\lambda}{NA} \tag{9}\]  
and thus almost equal to the minimum beam diameter according to (7). Using Equations (9) and (5) and the relation \(M^{2}=\frac{\omega_{0,real}}{\omega_{0,theory}}\), we can calculate the expected minimum \(M^{2}\) achievable in our setup to be  
\[M^{2}_{min}=\frac{1.14\lambda/NA}{\lambda/NA}=1.14 \tag{10}\]  
Therefore, we can conclude the following theoretical resolution of our setup of \(\Delta x_{Airy,D4\sigma}=5.6\)\(\upmu\)m and \(\Delta z_{Airy}=55.6\)\(\upmu\)m for the blue laser and \(\Delta x_{Airy,D4\sigma}=6.7\)\(\upmu\)m and \(\Delta z_{Airy}=67.1\)\(\upmu\)m for the yellow laser, respectively.
###### Abstract
Similarly, the application of a \(k\)-nearest-neighbor algorithm (KNN) is an option that groups points based on the distance of the \(k\)-number [16]. In addition, classification with a mixed Gaussian model and an expectation/maximization (EM) algorithm also show great potential [26]. Improper segmentation leads to the same grouped segment containing points from different leaves, especially at adjacent points on the leaf edge. Unfortunately, the above-mentioned methods cannot prevent false segmentation because a grouping method based on the selection of PCDs within a specific distance from the centroid is inelastic [10; 24; 25], resulting in the inability to group PCDs that are not evenly distributed; further, sometimes the performance of the results is data-dependent [16; 24]. On the other hand, the \(k\)-means method is more elastic in terms of grouping, with flexibility in choosing the cluster sizes. In addition, in most studies, the discussion and verification of segmentation accuracy, which affects the cumulative accuracy of LAD calculations, have been omitted. Therefore, this study aimed to develop a method for developing resilience in region segmentation to achieve a higher leaf angle and LAD estimation accuracy based on the \(k\)-means method using LiDAR-derived PCDs. In addition, two standardization procedures are provided: (1) verifying the segmentation accuracy to check whether the leaf clusters are correctly segmented; and (2) verifying that the verified leaf angles match the actual leaf angles.
# Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management
## 3 Counter-UAS Sensors Modeling
### Microphone Sensor and RF Sensor
\[A_{gr}(\mathrm{dB})=4.8-\left(2\cdot\frac{h_{m}}{R}\right)\cdot\left[17+\left( \frac{300}{R}\right)\right] \tag{42}\]  
where \(h_{m}\) is the average height of the propagation path above ground in meters, and \(R\) represents the distance from the drone to the receiver, also in meters. An object should be considered as a shielding obstacle (barrier) if: it meets a surface density of at least 10 kg/m\({}^{2}\), it has a closed surface with no large cracks or gaps and the horizontal dimension of the object perpendicular to the line connecting transmitter-receiver is greater than the wavelength. As the simulator is going to operate in real spaces that are filled with objects, it is assumed that the barrier losses are 3 dB. Finally, there may be other types of attenuation such as those due to foliage or housing. Losses of 3 dB are assumed. The reception noise is assumed to be the microphone noise (human noise + atmospheric noise + natural interferences). This noise predominates over the thermal noise. Thus, the power of the audio noise is assumed to be a constant (\(P_{N\_ellec}\)). Once the signal-to-noise ratio is obtained, detection is generated. Logistic regression was used to determine the probability of detection. If the signal-to-noise ratio exceeds the sensitivity of the sensor there is detection, the model used is as follows:  
\[PD=\frac{1}{1+e^{-\left((\frac{\mathrm{dB}}{R})-Sensitivity\right)}} \tag{43}\]  
The generation of whether there is detection or not is confirmed by generating a uniform random variable and comparing it with the probability of detection:  
\[Detection=\left(rand(0,1)\leq PD\right) \tag{44}\]  
Later, false alarms generation and the generation of measurement positions is performed. These generations are the same as those of the quasi-monostatic radars, so the explanation of their procedures can be seen in that section. It should be noted only azimuth measurements are obtained (through the measurement of the angle of arrival), as range measurement from acoustic signals would demand the performance of multi-static/trilateration procedures.
# The Multiband over Spatial Division Multiplexing Sliceable Transceiver for Future Optical Networks
## 2 Enabling 6G Technologies
### Spatial Division Multiplexing Technology  
SDM technology encompasses diverse options, ranging from the deployment of bundled SSMFs to the utilization of specialty fibres such as few-mode, and multi-core fibres among others [31; 32; 33]. The deployment of new speciality fibres will require to establish a new transmission ecosystem and infrastructure translating to high CAPEX efforts. Hence, in the short/medium term, SDM could be exploited considering bundle of fibres in order to reach the high-capacity targets that 6G will pose, postponing other SDM solutions based on different fibre types for the long-term. These fibre technologies collectively open up the exciting potential for a substantial surge in the spatial-channel density within optical fibres, transcending the limitations of today's single-mode fibre solutions. The use of speciality fibres in the long-term will provide a more efficient and versatile approach to SDM, offering:  
1. Enhanced spatial-channel density by supporting multiple channels within a single fibre.
2. Reduced crosstalk due to speciality fibres are designed to minimize optical signal crosstalk between channels.
3. Simplified network architecture by consolidating multiple channels into a single fibre.
4. Improved spectral efficiency by allowing for the transmission of more information within the available bandwidth.
5. Lower latency due to the availability of more direct and efficient paths for data transmission, enhancing the overall performance of the network.
6. Future-proofing providing high-scalability and efficiency for accommodating increasing demands.  
Among the different highlighted speciality fibres, few-mode fibres, where a single, larger-area core accommodates multiple modes each propagating distinct information, presents some limitations. Specifically, modal dispersion, modal interference and high differential mode group delay can appear and limit the performance when co-propagating several modes [19; 34]. MCFs, characterized by its multiple cores enveloped within a common cladding, can offer promising capacity upgrades in an efficient way [35]. However, optical signal XT, which can appear between cores, must be addressed and mitigated as part of the system's design and implementation [18; 36; 37]. To ensure the optimal performance of SDM technology, it is essential to align the transmission approach closely with the specific fibre design chosen for the application. In this paper, we will focus on MCF transmission, which seems to be one of the most efficient way to realize SDM, as a promising long-term solution that combined with MB technology can achieve the 6G stringent targets/requirements.
## 6 Application of the Method on Two Earthquakes in Greece
### Efpalio Earthquake
#### 6.1.2 Co-Seismic Displacement Computations and Their Accuracy  
The different horizontal (E-W, N-S and Up coordinate differences using the PPP method are plotted in Figure 1. The time on the \(x\)-axis is expressed in Modified Julian Date (MJD). The coordinate difference time series are approximated using the method found in Section 4. The result shows systematic offsets in both the E-W and the Up directions (up to 40 mm). The RMSE of the daily solution is characterised by 3 mm, 6 mm, and 11 mm in N-S, E-W, and Up directions, respectively.  
To estimate systematic offsets between the mean coordinates before and after the earthquake (i.e., co-seismic static displacement), we used an anharmonic analysis, but with estimating constant members. Additionally, the critical day of 18 January 2010 (MJD 55214) was excluded from the analysis. The result of this analysis is shown in Figure 3, and the estimated differences of the individual components can be seen in Table 1 with RMSE computed using both approaches.  
The result of the most commonly used method for determining the magnitude of the displacement (i.e., the difference between the averages of the daily GNSS results before and after the seismic event) is also given in Table 1. It is evident that they differ (by +0.09 cm, +0.07 cm, -0.06 cm) from the result of the anharmonic spectral analysis. In the N-S direction, this is more than RMSE from the 1 st approach. Therefore, in some cases, there is a difference between determining co-seismic displacement from the differences in arithmetic averages and the differences in anharmonic spectral analysis.  
RMSE from the 1 st approach are less than or equal to 1 mm (0.2, 0.7, 1.0 mm for the components N, E, Up). This is an order of magnitude less than the usual positioning accuracy of PPP. RMSE from the 2 nd approach (4.8, 7.6, 13.8 mm for the components N, E, Up) are several times larger (24\(\times\), 11\(\times\), 13\(\times\) for the components N, E, Up). Therefore, they better correspond to the assumed accuracy of determining the difference from PPP positions.  
Figure 2: Geographical distribution of the seismic permanent observing stations (triangles). The permanent GNSS station EYPA is shown as square. The red circles are the epicentres of Effcalio earthquakes (from [13]).
## 1 Introduction  
Microbial fuel cells (MFCs) technologies have a potential application in simultaneous wastewater treatment and electricity generation [1; 2]. In fact, this technology is able to convert the chemical energy contained in organic and inorganic chemical compounds directly into electricity via microbial bioanodes [3; 4]. The performance of MFCs, in terms of current production and effluent treatment, show considerable variation due to the MFC designs and the electrode materials used. Many electrode materials have been tested to treat domestic or industrial wastewater in MFCs [5]. However, weak anodic performance, primarily limited by the extracellular electron transfer between the exoelectrogenic bacteria and the anode, represents a major problem for the scaling-up and future practical application of MFCs. Thus, efficient modification of anode electrodes is predicted to improve power output in MFCs [6; 7; 8; 9]. For instance, carbon felt coating with akaganeite (b-FeOOH) was found to improve the electrochemical performance of a MFC by increasing the peak current density of the oxidation reaction from 0.7 to 6.1 A/m\({}^{2}\). A maximum power density of 504 mW/m\({}^{2}\) was obtained with the anode modified MFCs, which was 2.3 times higher than that with the unmodified anode [8]. [PERSON] et al. (2017) reported a 3.6-times higher power density with carbon felt anodes modified with graphene oxide (GO)-zeolite composite (GZMA), than that of the unmodified anodes. The highly porous structure and increased hydrophilicity of zeolite with a few layers of thin GO offered better adhesion and higher surface area for bacterial cells to attach to the electrode, which improved anodic kinetics resulting in a greater performance of the MFC.  
Although considerable progress has been made recently regarding anode modification for the improvement of MFC energy recovery efficiency, most of this research was restricted to synthetic media only. In contrast, the bioanode designed to treat real wastewater still faces practical barriers such as low current density leaving a large gap between real raw wastewater and synthetic media [10].  
The present study developed practical, easily fabricated, and low-cost modified electrode materials by Linde Type A (LTA) zeolite and bentonite to assist the bioelectrocatalytic oxidation of tannery wastewater by using an acclimated sludge obtained from a wastewater treatment plant. To the best of our knowledge, this was the first approach to combine both acclimation of activated sludge and electrode modification for the treatment of full strength tannery wastewater without any supplements of salt or vitamins in the BESs.
## 4 Activation Methods of RP
### Biomineralization Activation  
Biomineralization is the biological process by which living organisms precipitate mineral phases through their metabolic activities in the surrounding environment [169; 282; 283]. Mineralization in bacterial systems occurs through either biologically controlled (BCM) or biologically induced (BIM) processes [282; 283]. The process of microbial-induced carbonate precipitation (MCIP) is linked to BIM and is the most extensively researched aspect of biomineralization [283; 284].  
MICP is a phenomenon where calcium carbonate crystals are formed as a result of the interaction between metabolites produced by microbes and a calcium-rich environment. In alkaline conditions, various bacterial species precipitate carbonates by diverse processes [169; 282; 285; 286]. Nearly all bacteria have the ability to perform MICP at different rates and the MICP process naturally happens under normal settings [285; 287; 286].  
[PERSON] et al. [169] utilized gas-solid carbonization, liquid-solid carbonization and microbial mineralization to activate the recycled hardened concrete powder (RHCP) under the normal temperature and pressure environment with a CO\({}_{2}\) volume fraction of 19.98%. For MICP, during the activation of RHCP, a Bacillus strain with the ability to secrete carbonic anhydrase was added to the CO\({}_{2}\) carbonization system of RHCP. They stated that mineral composition resulting from the DC-RHCP (RHCP treated with gas-solid carbonation), LC-RHCP (RHCP treated with liquid-solid carbonation), and MM-RHCP (RHCP treated with microbial mineralization) were identical. Conversely, the microstructure of RHCP was shown to vary depending on the activation method used. They observed that the MM-RHCP's surface was filled and coated with calcite-type CaCO\({}_{3}\), resulting in an enhancement in particle size distribution.  
According to [PERSON] et al. [169], the biological deposition exhibited a lower crystal size and a spherical form, which was distinct from the chemically deposited material. Meanwhile, the recently created biologically deposited calcium carbonate might serve as the nucleation site and stimulate the production of C-S-H gel due to its smaller crystal size and bigger specific surface area. They stated that the addition of MM-RHCP can serve as sites for nucleation, promoting the development of hydration products during the curing process. This, in turn, speeds up hydration and enhances the strength of mortar or concrete. Furthermore, bacteria have the ability to assimilate CO\({}_{2}\) from the atmosphere or mineralization chamber, thereby expediting the process of CO\({}_{2}\) and calciumion mineralization. This results in the formation of additional calcium carbonate, which fills the existing fractures. Thus, the performance of MM-RHCP was superior to that of DC-RHCP and LC-RHCP to some degree.