esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Best Practice
### Data Products from UAV Sensing Systems
We think that the ultra-high resolution of UAV images can be used for precise measurements needed by smart farming [203], agricultural [204] and phenotyping applications [205; 206; 207; 42] and with the powerful tools provided by object-based image analysis (OBIA; [208; 209]) for classification tasks (e.g., [48]). Additionally, UAVs allow mapping in terrain that is hard to access with proximal sensing methods such as mangroves or high canopies such as forests [72; 73; 74; 201]. In addition, the combination of high-resolution 3D data and spectral data enables new segmentation, complementing, and combination approaches for data analysis [210], as shown e.g., in forests [72; 73] and agriculture [211; 212]. Still, approaches and algorithms that analyze the large amounts of multi-dimensional, high-resolution UAV data are a major bottleneck that should be focused on in future.
## 5 Numerical Results
### Effects of System Parameters
Specifically, by increasing the street width the UAV LoS blockage probability drastically increases, see Figure 7a. The rationale is that this leads to much larger areas of LoS visibility zones, see Figure 2. At the same time, one may observe that by increasing the street and building block widths, the considered area increases as the number of streets and building blocks in both horizontal and vertical directions are kept constant. Thus, logically, larger building blocks dimensions lead to higher UAV LoS blockage probability, see Figure 7b. Nevertheless, this effect is attributed to the increase of the considered area.
# Fooling in Membrane Distillation, Osmotic Distillation and Osmotic Membrane Distillation
## 4 Characterization Techniques for Fouling Analysis
### Microscopy Techniques
#### 4.2.1 Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDX)  
SEM is one of the most used techniques to study both membrane surface and its cross-section [90; 91; 92; 93]. In this technique, with the electron/sample interactions and the reflected electrons high resolution topographical images of fouled membranes are provided [94]. First, liquid nitrogen is used to freeze the membrane sample that is subsequently broken in small pieces [2; 15]. Then the sample is coated with a layer of gold, carbon or platinum by sputtering to render it electrically conductive [15; 95]. It must be pointed out that due to both immersion in liquid nitrogen and coating, the fouled membrane sample limits the use of this technique for fouling characterization because it gives the sample some artifacts and damages changing the fouling layer characteristics [2; 15; 96]. More details on this technique can be found in [2; 15].  
In addition, energy dispersive X-ray (EDX) spectroscopy can be applied together with SEM to analyze the composition and crystallographic nature of the membrane sample [94]. The principle of this technique relies on the interaction of a source of X-ray excitation and the metal coated sample. In this case, when an incident electron bombards an atom of the sample and knocks out an electron from the outer layer of the metal coated sample, a vacancy or hole is left in this layer. If an electron from another layer fills this vacancy (electron transitions), then X-rays are emitted. These transitions are characteristic of each chemical element [94].
# Intelligent Network Service Optimization in the Context of 5G/NFV
## 4 Methodology of MAPE's Analysis Service
### Optimization Actions  
In our research, the optimization actions of the OE are focused on the horizontal scaling of services by handling computational and networking resources provided by the NFVI. Thus, the MANO (OSM) optimizes the life-cycle management of the NS/VNFs based on dynamic allocation of fundamental virtualization resources such as containers, VMs, and virtual networks. In cases of anomalies, congestion, or overutilization, the NS will be scaled out and another VNF will be aggregated. On the other hand, in cases where the NS has already been scaled out and is underutilized, the AS module, acknowledging the misplacement of resources, suggests the necessary scale-in actions and the additional VNF is scheduled for deletion, hence the respective resources are released.  
The optimization actions are identical in both the DL and RL solutions (see Table 3). However, the way that each of these approaches reaches a decision is inherently different. The DL solution classifies an incoming vector of measurements and decides whether an optimization action should be executed, while the RL solution, having modelled the environment, maps the incoming vector of measurements to a state and performs the optimal action.
# Ultrafast and Wideband Microwave Photonic Frequency-Hopping Systems: A Review
## 2 Microwave Photonic Frequency-Hopping Systems
### Bias Control in Dual-Drive Mach-Zedmder Modulater for Frequency-Hopping Signal Generation  
To achieve a faster frequency-hopping speed, Pockels effect in a dual-drive Mach-Zehnder modulator (DD-MZM) is used [28], as illustrated in Figure 7a. In this approach, a reference microwave signal \(f_{s}\) with amplitude \(V_{0}\) is injected into one arm of the DD-MZM, while a hopping code sequence \(S(t)\) is applied to the other arm of the DD-MZM. The DC ports of the two arms are driven by a DC power supply to introduce phase difference. When a hopping code sequence \(S(t)\) with an amplitude of \(V_{s}\) is applied to DD-MZM, the transmission function of the frequency-hopping system can be expressed as,  
\[T=\frac{1}{2}+\cos\left[\frac{\pi(V_{0}\cos(2\pi f_{s}t)-V_{s}S(t)-V_{DC})}{V_ {\pi}}\right] \tag{1}\]  
where \(V_{\pi}\) is the half wave voltage of the DD-MZM and \(V_{DC}\) is the DC bias difference between the two arms. As shown in Figure 7b, if \(V_{DC}\) is at the quadrature transmission point, the output RF signal is at the original reference signal frequency \(f_{s}\) due to the linear region in the transmission curve; while biasing at the minimum transmission point will lead to a doubling in output RF signal frequency (i.e., \(2f_{s}\)) due to the folding in the transmission curve. Thus, by setting the static DC bias at minimum transmission point and dynamically apply \(S(t)\) to shift the bias to quadrature point, a frequency-hopping signal is resulted and can be expressed as,  
Figure 6: (**a–c**) Measured temporal waveforms of the frequency-hopping signal under different injection conditions. (Insets) Corresponding hopping code sequence \(S(t)\). (**d–f**) Recovered instantaneous frequency corresponding to (**a–c**), respectively (with the courtesy of Dr [PERSON]).  
\[i(t)=\left\{\begin{array}{ll}\frac{1}{8}RE_{0}^{2}-\frac{1}{2}RE_{0}^{2}I_{0}(m)+ RE_{0}^{2}J_{2}(m)\cos(4\pi f_{s}t),&S(t)=1\\ \frac{1}{2}RE_{0}^{2}+RE_{0}^{2}J_{1}(m)\cos(2\pi f_{s}t),&S(t)=0\end{array}\right. \tag{2}\]  
where \(R\) is responsivity of the PD and \(m=\pi V_{0}/V_{\pi}\) is the modulation index. As shown in Figure 7c, the temporal waveform of the frequency-hopping signal indicates that the frequency is hopping between 12 GHz and 24 GHz when a 64-bit 1 Gbps pseudorandom binary sequence (PRBS) hopping code sequence is used. The hopping speed is measured to be 1 ns in this experiment. Since the DD-MZM has a higher modulation efficiency at the quadrature transmission point than that at the minimum transmission point, the generated frequency-hopping signal at \(f_{s}\) will always be stronger than the signal at \(2f_{s}\), as shown in Figure 7c,d. Furthermore, the frequencies in the frequency-hopping signal will always have a fixed relationship of \(f_{s}\) and \(2f_{s}\).
## 2 Research Objects and Numerical Methods
### Physical Properties and Boundary Conditions  
Three-dimensional steady aerodynamic calculation of the designed S-CO\({}_{2}\) axial turbine is completed by CFD software NUMECA, in which a single flow channel is adopted. In this paper, in order to accurately simulate the physical properties of the working fluid, the explicit equations in the Helmholtz energy equation, the modified Benedict-Webb-Rubin equation of state (abbreviated as MBWR equation) and the extended corresponding state (ECS) model are used to describe the characteristic parameters of the state points, based on a large amount of carbon dioxide physical property data. The MBWR equation contains 32 parameters, the accuracy of fitting pure fluids is high, the pressure deviation and density deviation are small, and the pressure and temperature applications are wide. The improved Benedict-Webb-Rubin state equation is as follows [24].  
\[p=\sum_{n=1}^{9}a_{n}\rho^{n}+\exp\Big{(}{-\delta^{2}}\Big{)}\sum_{n=10}^{15} a_{n}\rho^{2n-17} \tag{8}\]  
where \(\delta=\frac{\rho}{\rho_{c}}\) (\(\rho_{c}\) is the critical density). \(a_{n}\) (\(n=1\),\(2\)\(\ldots\)\(15\)) is temperature-related characteristic parameters.  
The Kriging surrogate model is used to construct the physical property library within the calculation range (5 MPa-20 MPa). Since the physical properties of carbon dioxide change very sharply near the critical point, the difference point near the critical point is locally encrypted.  
A total pressure of 15 MPa and a total temperature of 773.15 K are set for the first stage nozzle inlet as the boundary conditions, and a static pressure of 9.5 MPa is set for the third stage rotor outlet. The rotational speed of the rotor fluid zone at each stage was set at 10,000 rpm. The interface between the nozzle and the impeller is set as the coupling interface, and the mixed model Full Non Matching Mixing Plane is used to couple the flow of the stator and rotor. The nozzle wall surface of the whole model is set absolutely static, the impeller wall surface of the whole model is set relatively static. The upper and lower walls of all areas are insulated walls, meeting the no-slip flow conditions.  
The double-precision numerical method is used to solve the three-dimensional Reynolds average N-S equation. The CFL value (Courant-Friedrich-Levy number) of the calculation model is set to two, and the convergence criterion number is \(10^{-6}\). In addition, acceleration techniques such as implicit residual smoothing, multi-grid and full multi-grid are used in the calculation process, which greatly improves the convergence speed of the calculation [25].
## 7 Challenges and Future Perspective  
Submerged liquid cultivation is mostly investigated in flasks and lab-scale bioreactors, and there is not enough research on the behavior of such systems in upscaled industrial applications for the commercial production of fungal biocompounds. There is not enough knowledge of how macrofungi and the substrates might behave on such a scale. Although Basidiomycetes and Ascomycetes are highly valued as sources of bioactive compounds, only a few species have been thoroughly studied, and many still remain chemically unexplored [152]. Practical application of the biotechnological processes using mycelial cultures depends on the development of industrial technologies for large-scale submerged cultivation and downstream processing to ensure commercial success. Physiological and biochemical mechanisms that regulate the biosynthesis and secretion of bioactive substances as well as the techno-functional properties of fungi and their products need to be further studied and understood if optimized production is to be achieved [17]. Of course, the existing research on yeast and mold behavior in large-scale fermentations can provide useful insights and strategies for macrofungi liquid cultures. Furthermore, the use of engineering tools such as mathematical models and optimization techniques has not been properly utilized and integrated into the process of submerged cultures yet. Tools such as computational fluid dynamics and the implementation of novel sensors in the bioreactors hold promise for calculating how the morphology of broth rheology and mass transfer affect the overall process [172]. Deep learning, machine learning, and the use of AI in general are constantly gaining popularity in all areas of industrial production and will certainly have an impact on the improvement of secondary metabolite biosynthesis from fungi [234]. Molecular biology, genetic engineering, bioinformatics, next-generation sequencing, and multi-omics have an important role in increasing the production potential of macrofungi on a commercial scale [235; 236].
# A Perspective on Emerging Inter-Disciplinary Solutions for the Sustainable Management of Food Waste
## 4 Food Waste Management, Regulations, and Biological Treatment Technologies
### Initiatives Supported by Omics and Bioengineering Disciplines
Furthermore, process improvement and increased biogas production efficiency may be possible via the discovery of metabolic pathways within microbial communities that may be uncovered through the characterization of intracellular metabolites generated during anaerobic fermentation [146]. Since metabolomics is a comprehensive study that identifies and quantifies all of an organism's metabolites in a specific environment under specified conditions, these metabolites are frequently identified and quantified using a variety of chromatographic methods, including GC-MS, NMR, Fourier-transform infrared (FTIR) spectroscopy, and HPLC [147]. In a stable and degraded methanogenic reactor, [PERSON] et al. [147] successfully compared microbiome metabolite profiles and provided critical context for the metabolomics approach by finding changes in intracellular metabolites implicated in the Embden-Meyerhof and pentose phosphate processes. Several researchers have also integrated omics and bioengineering disciplines to improve anaerobic digestion. For example, laboratory-size digesters were used to mimic possible shocks to a wastewater treatment plant's anaerobic digestion process [126]. Metagenomic and metabolomic techniques were then employed to characterize the influence of operational shocks on digestion parameters during biogas generation. These omics tools could characterize the microbial makeup and metabolic activity throughout the anaerobic digestion process. In summary, the value of using omics methodologies in an anaerobic digestion process setting cannot be overstated. In light of the information presented above, the purpose of this section is to try to summarize the significance of using a variety of omics technologies in the process of producing biogas. While further research must be done to fully realize the potential of this area of research, it is already well established that combining omics and other technologies gives a significant potential for boosting the efficiency of creating biogas from various wastes. This might be a beneficial development, particularly concerning the management of a wide variety of wastes. Moreover, the generation of biogas from organic matter is becoming a more attractive source of bioenergy. Recent breakthroughs in omics technology have enhanced our capacity to identify and analyze genes, encoded transcripts, proteins, metabolites, and metabolic pathways involved in anaerobic digestion. Finally, as previously stated, the co-application of omics and other technologies may result in a greater knowledge of the process of anaerobic digestion, as well as the generation of biogas that is more efficient and cost-effective to address the difficulties posed by global food waste.
# Sensors and Communication Simulation for Unmanned Traffic Management
## 5 Proposed Simulation Models for Key Elements
### Drone Control, Navigation, and Trajectory.
On the other hand, non-cooperative sensors detect the drone in its real position which, in a simple approximation, would correspond with the estimated position subtracting the navigation error. We model the navigation error in each of the three dimensions, as a time-correlated Gaussian additive error with zero average. This process is depicted in Figure 6. Although it is a simplification of the actual navigation and control loops, this process is realistic enough to enable the assessment of tracking and conformance monitoring functions within UTM. After the position is updated, the real and estimated states are broadcasted through logical channels of the message bus so that this information can be used by other agents. In the previous paragraphs we described the drone's behavior in the absence of contingencies. However, the planned trajectory can be modified after receiving commands through C2 command (from the GCS) or by receiving contingencies through the actions bus. In either case, the predicted trajectory would be recomputed according to the following expected typical drone reactions:  
* LOL (loss of link) or RTH (return to home) order from GCS. A straight-line trajectory to the take-off point would be performed. * LOG (loss of GPS) or LOB (loss of battery). An immediate landing would be performed. * LOC (loss of control). A random trajectory using a random walk would be simulated until battery exhaustion. * Stop/resume order from GCS. Trajectory would stop and drone would hover in place until it is resumed. * Change to manual/automatic order from GCS. It would change drone control error model parameters to the appropriate ones. * Deviate flight plan from GCS. A new trajectory would be computed and simulated from the drone's position to the starting point of the new trajectory and would continue by simulating the new one.
## 6 Discussion on Gaps, Challenges, and Links to Future Works
### Application of Available Excuation Software to Wildfire Evacuation
#### 6.5.5 Data Compatibility and Interoperability  
Comparing and integrating simulation results from different software packages can be challenging due to differences in data formats and software requirements. For instance, macroscale and mesoscale models may use different GIS software and data structures, making it difficult to directly compare output results or share data between models seamlessly.
## 3 Results and Discussion  
### Simulation Data Set Construction  
In order to verify the performance of the AP-CNN in OAM mode detection for similar distributions of multi-mode vortex beams, four pairs of OAM modes, namely, {1, \(-2\)} and {1, \(-2\), \(-5\)}, {1, \(-2\), \(3\), \(-5\)} and {\(-2\), \(3\), \(-5\)}, {4, \(-4\)} and {2, \(-6\)}, {6, \(-6\)} and {9, \(-3\)}, are selected, as shown in Figure 5. The light intensity distributions in four columns are similar to each other. In addition, in order to test the detection performance of a single-mode vortex beam with a large topological charge interfering with the plane wave, a plane wave interferogram dataset of a single-mode vortex beam is constructed, choosing eight types of samples with large topological charges, i.e., \(\pm 17,\pm 18,\pm 19,\pm 20\), as shown in Figure 6.  
The wavelength of the OAM communication system is 0.6328 \(\upmu\)m and the beam waist radius is 0.3 m. For the comparison experiments under different turbulent conditions, the transmission distance is fixed and six different atmospheric refractive index structure constants, \(C_{n}^{2}\), are selected: \(1.0\times 10^{-14}\)m\({}^{-2/3}\), \(3.0\times 10^{-14}\)m\({}^{-2/3}\), \(5.0\times 10^{-14}\)m\({}^{-2/3}\), \(1.0\times 10^{-13}\)m\({}^{-2/3}\), \(3.0\times 10^{-13}\)m\({}^{-2/3}\) and \(5.0\times 10^{-13}\)m\({}^{-2/3}\). For comparison experiments at different transmission distances, the \(C_{n}^{2}\) is fixed and six different transmission distances are chosen: 500 m, 1000 m, 1500 m, 2000 m, 2500 m, and 3000 m. When simulating the atmospheric turbulence channel, the power spectrum inversion method is used to decimate the transmission distance in order to obtain ten phase screens with certain intervals. For each transmission  
Figure 5: Light intensity distribution of a similar multi-mode OAM beam.  
intensity maps are generated for each OAM mode. In this way, a total of 16,000 light intensity distribution maps are included in the hybrid dataset. This is then divided into a training and a test set at a ratio of 8.2 (12,800 images in the training set and 3200 images in the test set).
###### Abstract  
The seismic performance of prefabricated reinforced concrete block masonry shear walls (PRMSWs) was studied. Five PRMSWs were tested under cyclic loading to evaluate the effect of the axial compression ratio and the distribution of the vertical rebar on the inelastic behavior. Based on the experimental results, the lateral load capacity, failure mode, lateral drift, ductility, stiffness degradation, energy dissipation, and the seismic performance stability of the specimens were analyzed. The finite element analysis of the specimens was conducted with ABAQUS, which agreed quite well with the laboratory findings. Relevant results showed that PRMSW exhibited favorable ductility and energy dissipation. The increase of the compression ratio led to stiffer, but more brittle, inelastic behavior of the specimens that had higher flexural strength. The shear walls that had concentrated vertical rebar at the sides exhibited relatively higher load capacity and less ductility compared to the walls that had evenly distributed rebar. The inelastic lateral drift limit of the PRMSW could be assigned \(1/120\). The equivalent viscous damping ratio of the PRMSW was \(9\)-\(13\%\) at ultimate load. These results provide a technical basis for the design and application of the PRMSW structures.  
prefabrication construction technology; reinforced masonry shear wall; experimental study; numerical simulation; flexural failure; seismic performance