esa-sceva/satcom-chunk-collection huggingface dataset:
# Beyond 5G Fronthaul Based on FSO Using Spread Spectrum Codes and Graphene Modulators
## 3 Graphene Modulator for Next-Generation Fronthaul Networks
### Design, Optimization, and Numerical Simulations
By using the values obtained via numerical simulations for the chemical potential for each graphene amount, the power consumption per bit (Joules/bit) will be, in which \(\rho_{ON}>\rho_{OFF}\),  
\[E_{\frac{J}{\text{M}}}=\frac{1}{2}\frac{\left(Q_{ON}^{2}-Q_{OFF}^{2}\right)}{2 C}=\frac{1}{4}\left(\rho_{ON}^{2}-\rho_{OFF}^{2}\right)\frac{e^{2}h_{diel}S}{ \varepsilon_{0}\varepsilon_{r}}, \tag{2}\]  
where \(Q_{ON}\) and \(Q_{OFF}\) represent the amount of charge that correspond to the ON and OFF states, \(C\) is the modulator's capacitance, \(\rho_{ON}\) and \(\rho_{OFF}\) are the carrier concentrations for the graphene's chemical potential equivalent to the ON and OFF states, \(e\) is the electron charge, \(h_{diel}\) is the dielectric thickness, \(\varepsilon_{0}\) and \(\varepsilon_{r}\) are the vacuum and relative permittivity, respectively, and \(S\) represents the capacitor's area, which corresponds to the area on the top  
Figure 3: (**a**) Schematic representation of the homogenization approximation technique for the resonator waveguide. (**b**) Real (green) and imaginary (black) part of an effective index for the graphene modulator. Inset: first derivative of real (green) and imaginary (black) parts of refractive index for 0.4–0.48 eV. (**c**) Front view of a graphene ring-resonator coupled with a bus waveguide. surface of the resonator. Lastly, the \(-3\) dB bandwidth of the proposed graphene modulator is addressed. To do so, a figure-of-merit (\(FOM\)) of the modulator is defined as [82]  
\[FOM=\frac{FWHM(GHz)}{Power\ Consumption\left(\frac{E}{bit}\right)\times Area(\mu m^ {2})}, \tag{3}\]  
where \(FWHM\) is the full width at half-maximum pulse in the frequency domain, \(E\) is the energy required for switching, and \(Power\ Consumption\) and \(Area\) are the power-per-bit consumption and full area of the device, respectively. The \(FOM\) provides an expression of the efficiency of the device, that is, a measure of its capabilities and performance. This is valuable for optimization of the design of modulators. Careful design and optimization of the parameters can provide an incredible degree of freedom in the modulator's features, resulting in singular performances. Then, the optimization procedure is carried out by optimizing the modulator's design for both geometric and physical parameters. The parameters along with some performance metrics are summarized in Table 1. Note that \(\mu_{OFF1}\) is the initial value of the chemical potential for the OFF state, whereas \(\mu_{OFF2}\) is the value after optimization. In a similar fashion, \(Loss_{1}\) is the total resonator loss (bending and absorption losses) before the optimization procedure, and \(Loss_{2}\) is the optimized value. The same procedure is applied for the remaining parameters. The optimization renders a more energy-efficient device with a larger bandwidth (due to lower \(\mu_{OFF}\) and \(\mu_{ON}\)).
## 4 Evaluation
### Routing Fairness under Heterogeneous Buffer Capacity  
Although portable handheld devices are roughly homogenous in terms of energy and processing power, motivation derived from incentive benefits may cause users to allocate different amounts of storage space to the technology--for instance, some users may want to contribute with more storage space in order to gain more incentives. Hence, a robust fair routing solution should be able to treat a device fairly despite the resources allocated to the network. Section 2.4.3 discussed how inferring burden from buffer occupancy affects routing decisions when users do not allocate the same amount of storage space to the network. Specifically, devices that have more buffer capacities are subjected to unfair treatment due to the burden detection approaches of existing fair routing solutions.  
Therefore, it is important to have an insight on how the proposals perform in scenarios characterised by heterogeneous buffer capacity. To configure different buffer sizes, we generate a second node group from each existing group in the (ONE simulator's) settings file and change their buffer size from 10 MB to 2 MB. The resulting fairness and throughput, before and after assigning different buffer sizes to the nodes, are shown in Figure 27.  
With CCAF, nodes that have higher buffer capacity are likely to receive more messages since they often present less buffer occupancy. Such nodes are thereby subjected to more burden and energy consumption, as compared with other nodes of the same popularity level but less buffer capacity. This unequal distribution in resource utilization among nodes results in a drop in fairness, as indicated in the figure. FLARoute, on the other hand, determines node burden from the number of transmissions, a major source of energy consumption in routing. Hence, fairness is not degraded despite differences in buffer capacities. Unlike CCAF that becomes even more unfair under higher data traffic, FLARoute also provides a more stable fairness and still maintains a higher throughput.
# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 5 Learning-Based Asynchronous Stream Sensing
### Asynchronous Stream-Sensing Network
#### 5.2.1 Inception Layer-Based Extractor  
This extractor consists of 'Inception layer-based Block' and '3 Dto2D Block'.  
**Inception layer-based Block**: The inception model is one of the most popular models for image classification and object detection in computer vision [37,38]. It is well known that the model is efficient due to its low computational load with a small number of model parameters. Hence, this model is appropriate for our ASensNet, which requires limited energy consumption due to the limited battery of UAVs.  
For this block, we exploit an inception module and a grid reduction module in [38], and then stack both modules. Then, some dropout layers are inserted into the stacked network to prevent overfitting.  
Finally, from 'Inception layer-based Extractor', subnetworks provide features \(\delta_{\text{R}}\) and \(\delta_{\text{I}}\) for \(\mathbf{I_{\text{R}}}\) and \(\mathbf{I_{\text{I}}}\), respectively.  
**3 Dto2D Block**: This block is to shrink the dimension of \(\mathbf{I_{\text{R}}}\) and \(\mathbf{I_{\text{I}}}\) from 4D to three dimensions (3D) since two-dimensional (2D) convolution layers in the inception module can deal with 3D matrices. This block consists of some 3D convolution layers that handle 4D matrices. After \(\mathbf{I_{\text{R}}}\) (or \(\mathbf{I_{\text{I}}}\)) passes through 3D convolution layers, the result matrix is reshaped into a 3D matrix.  
Through 3D convolution layers, this block is expected to capture temporal information from the input data. According to [39], 3D convolution layers model temporal information better than 2D convolution layers. The resulting matrix of this block can preserve temporal  
Figure 6: Asynchronous stream-sensing network (ASensNet).  
information. Therefore, '3 Dto2D Block' not only reduces the dimension of input data, but also allows the inception layer-based extractor to take into account the temporal information of a received signal.
## 1 Introduction
The use of a commercial fiber coated with non-polar PDMS has shown satisfactory results for the extraction of various pesticides. However, the extraction efficiency was lower for more polar pesticides, so the authors suggested the use of SPME fibers coated with less hydrophobic materials [43]. [PERSON] et al. 2021 have fabricated SPME fibers coated with boron nitride-modified multivalled carbon nanotubes for the extraction of organochlorine pesticides from agricultural samples by using SPME coupled with GC/ECD. Their method proved to be efficient and reliable for determining these residues in fruits and vegetables; it was also compared to three commercial fibers and gave higher extraction performance and reproducibility [44]. A method for the preparation of SPME fibers coated with metal-organic gels was developed by a research group that used a hydrothermal growth technique and optimized the preparation process. This fiber exhibited good extraction efficiency and wide linearity for eleven pesticides, with detection limits between 0.001 and 0.052 ng mL\({}^{-1}\). These coated fibers proved to be unaffected after 100 consecutive extractions, with the potential for pesticide extractions from complex samples [45]. The present article aims to describe a low-cost and simple method for the preparation of an SPME device by coating silicone sealant onto stainless steel syringe needles. First, the needles used are cheap and commercially available; secondly, the silicone used as a sorbent material is easily purchased from any local market. These simple SPME needles should be a valuable alternative to commercial SPME holders and fibers, which are expensive and fragile. The obtained PDMS-coated needles were characterized, and then applied in the extraction of various pesticides from agricultural water samples by DI-SPME, followed by GC-MS measurements.
# Enhancing Space Management through Digital Twin: A Case Study of the Lazio Region Headquarters
## 1 Introduction
### Advanced Digital Technologies
The pandemic has accelerated the digitization of processes and highlighted the importance of advanced technology solutions to ensure efficiency, sustainability, and resilience. By allowing the accurate digital replication of buildings and infrastructure, DT enables more effective and predictive management, reducing operational costs and improving the quality of services offered. This makes DT a crucial tool to meet the challenges of the post-pandemic recovery era and to promote sustainable and integrated innovation in the AECO sector (Figure 3). The objective of the research is to develop an advanced digital strategy to optimize the management of corporate spaces, thereby improving operational efficiency, reducing costs, and minimizing environmental impact. The proposed approach employs IoT sensors to assess employee presence or workstation activation and uses innovative machine learning (ML) technologies to automate and refine building management. In this way, building managers can operate with greater awareness and efficiency, thanks to access to timely data and reliable forecasts. This allows them to make more informed decisions on space utilization and resource management. The novelty aspect of this research is the potential for developing a digital system that can autonomously manage an office building, from targeted air conditioning to access at workstations, based on user reservation data and sensor data. The aforementioned data are then processed and managed by an ML-based system, which will be discussed in greater detail in the following sections. Once trained, this system is capable of functioning autonomously.
## 4 Selecting a Suitable Metal Oxide
### Cost and Regenerability  
Metal oxide sorbents are primarily used for H\({}_{2}\)S removal from diluted streams. In the form so called guard beds they present own-standing units that are easily integrated into a process. In such applications regeneration of the sorbent is usually not considered as the spent bed is simply replaced once the sorbent saturated. This is typically the case for calcium based sorbents and, to a lesser extent, iron based sorbents that are relatively cheap such that the issue of regeneration loses significance and fresh sorbent is re-supplied when needed [110].  
However, regeneration of sorbent gains importance when the fresh sorbent is costly to supply and prepare or in operations with multiple beds that cycle between removal and regeneration. Regeneration is essentially the reversal of the sulfidation reaction, i.e., the conversion of the metal sulfide to the metal oxide. The gas that is formed in the regeneration step typically contains sulfur in the form of SO\({}_{2}\) which is then processed further using, e.g., the Claus process that separates sulfur and stores it in form of elemental sulfur or sulfuric acid [12]. Regeneration usually takes place under extreme operating conditions, i.e., high pressure and temperature. Regeneration agents include air, oxygen or steam to which SO\({}_{2}\) or CO\({}_{2}\) is added. Table 5 gives an overview of previous works on regeneration of used metal oxides, including operating conditions and regeneration agents that were employed. The number of cycles with little to no significant drop in the sorbent capacity is also reported in Table 5.  
The critical aspect of regeneration is the preservation of the removal efficiency of the sorbent. Changes in surface and pore structure during repeated sulfidation-regeneration cycles can lead to a loss of specific surface area and pore blockage [80] which in turn results in incomplete regeneration and eventually a reduction of the sulfur removal capacity. A common issue with the regeneration of sorbents for H\({}_{2}\)S removal is that during the sulfidation process, the metal oxide particles are fused together. This is due to the higher molar volume of the metal sulfide compared to the metal oxide. This means that in the sulfidation process, the particles slightly grow in size and some neighboring particles might squeeze into each other which results in the fusion of the particles, as schematically shown in Figure 2b. This limits the available contact surface and hinders the regenerating agents to reach to some parts of the spent sorbent, resulting in an incomplete regeneration. This issue is usually fixed by mixing the sorbent with an inert solid that does not react during sulfidation. In this way the increase of the solid volume is reduced and so the possibility of fusion of particles is lowered.
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 2 Related Work
### Traditional High-Precision Indoor Positioning  
[PERSON] et al. [10] made use of the error reduced by TDOA compared with TOA to initially improve the accuracy, and at the same time used the positioning results of TOA to exclude the virtual anchor points in TDOA, thus realizing a positioning method combining TOA and TDOA. The idea of secondary clustering is used to optimize the selection of base stations to further improve the positioning accuracy. However, its positioning accuracy is limited, and the average error is only 1.717 m. [PERSON] et al. [23] have eliminated the interference of the same frequency caused by the same frequency networking in the data transmission of positioning, and carried out the same frequency co-load of the positioning signal and the communication signal, which can achieve a higher positioning accuracy under the simulation. However, it has not been written into the 5G standard, so it is difficult to carry out large-scale promotion in real applications. The Ubisense positioning system [24] is a positioning technology that can integrate TDOA and AOA for integrated positioning, and it has the ability of direction finding to estimate the azimuth and pitch angle that other similar UWB systems do not have, which enables it to obtain a high positioning accuracy. In an environment where ultra-wideband (UWB) radio propagation is disturbed, its positioning accuracy and outliers will be greatly affected [25]. [PERSON] et al. [26] propose an RSSI-based weighting Algorithm to evaluate the wireless sensor network technology for the indoor localization. Specifically, they estimate the target position from two aspects: the weight range localizer and relative span exponential weight range localizer. In this way, they significantly improve the accuracy of indoor positioning. [PERSON] et al. [27] detail the reason that RSSI-based methods will fail in complex situations to layer power feature and channel response. They also present a deep study on their techniques, which involve resorting to finer-grained wireless channel measurements.
## 5 Results and Discussion
### The Validation Case
#### 5.1.1 Benchmark Case of E-STR without Char Recycling  
As the first step, a benchmark case with preliminarily modified E-STR model, which decreases length and changes fed configuration without char recycling, is analyzed in this section. The operating pressure of the base E-STR case is still the same as E-Gas at 28 atm. The purpose of making this benchmark process is to find out the difference between the two models by only changing the geometry of the gasifier.  
In E-STR, all the fuel is fed at the 2 nd stage; hence, concentrated injection generates stronger momentum, racing coal particles impinging on the opposite wall from the injector. This phenomenon can be observed from the distribution of particle resident times shown in Figure 2a. Compared with a transverse jet with lower momentum, the impinging jet induces more complex flow patterns, which affect subsequent chemical reactions and temperature distribution accordingly.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Validation Case** & **Reference [38]** \\ \hline \multirow{4}{*}{Mole fraction (\%)} & CO & 36.24 & 35.90 \\  & H\({}_{2}\) & 22.23 & 22.90 \\  & CO\({}_{2}\) & 12.69 & 12.20 \\  & H\({}_{2}\)O & 18.05 & 23.90 \\ \hline \hline Exit temperature (K) & 1803 & - * \\ \hline \hline \end{tabular}  
* It is estimated that the value is around \(\sim\)1600 K.  
\end{table}
Table 6: Exit syngas temperature and major species of base case.  
Figure 2: _Cont._  
The temperature contours in E-STR without char recycling and operating at 28 atm are shown in Figure 3a. There is a combustion zone upstream the impingement location on the opposite wall from the injector, where the stagnation and recirculation caused by the impinging jet provide the flame stabilization mechanism. Therefore, a \"hot spot\" exists in the vicinity of the impingement. It is observed that combustion reactions of the injected fuel mainly take place downstream the injection port, where the expansion of vessel diameter following the throat region serves as a flame holder and results in higher interior temperatures near the 2 nd stage injector. Further downstream, gasification surpasses combustion as the dominant reactions; hence, different temperature distribution prevails, so its temperature level is lower.  
Figure 2: Comparison of particle resident time (seconds) for (**a**) E-STR model and (**b**) E-Gas model.
# Nonlinear Control Design of a Half-Car Model Using Feedback Linearization and an LQR Controller
## 2 Methodology  
To design a controller for vehicle active suspension system, a mathematical model is required. An extensive study was conducted to select a model for active suspension system. The quarter-car model, half-car model and full-car model are widely studied in the literature. In this work, the half-car model was selected which is fairly accurate and relatively easier to handle. Furthermore, nonlinear dynamics were incorporated to capture realistic situation. Road bumps were modelled as external disturbances. The states, state variables were extracted from state equation, the state equations were nonlinear and road disturbances variables were clearly evident in those equations. Output and Input equations were derived and then this whole model was implemented in the Simulink toolbox. Feedback linearization for single input single output (SISO) systems is fairly standard and can be found in the standard text. However, for multi input multi output (MIMO) systems, the procedure is mathematically more involved. We followed the approach presented in ([PERSON], 1995) for that purpose. This procedure is carried out in such a way that a state feedback control law \(u\) and new set of variables \(z\) were defined. The overall system is further split into \(f(x)\) and \(g(x)\), \(u\) is further simplified in to E and S matrices which are derived after solving the Lie derivative matrices. The system was then converted in to normal form \(\zeta\) and \(\eta\). These computational details of the procedure are explained in the next section. Finally, the LQR controller was applied to the system to minimize the effect of disturbances by designing a gain matrix K. According to the transformed system A, B, C and D matrices were derived and road disturbances graph is designed and applied with controller and without controller and minimized effects were clearly seen in the states of system. The values were then compared with those presented in the past research papers and the results were proved to be better. The flow chart diagram to carry out this research work is shown in Figure 1.
# Dynamic Analysis and Security Characteristics of Carrier-Based Aircraft Arresting in Yaw Condition
## 2 Arresting Dynamics Model of Carrier Aircraft
### Drag Force Model of Carrier Aircraft  
Due to the complexity of the forces on the carrier aircraft in the process of landing and arresting, and in order to facilitate the analysis, the force on the aircraft in the process of facing the ship is simplified as follows [16]:  
1. The runway is a plane, and the aircraft's head is parallel to the runway plane;
2. In the process of arresting, the vibration after the engagement of the hook and cable is not considered, and the sum of forces in the cable (\(T_{L}\) and \(T_{R}\)) are shown in the exact opposite direction of aircraft movement;
3. The influence of the vertical height of the carrier aircraft on the arresting resistance is not considered.  
When centering and arresting (without considering the eccentric yaw), the arresting resistance of the carrier aircraft is as shown in Figure 2.  
Figure 1: Schematic diagram of carrier aircraft landing and hanging cable.  
According to Figure 2, the calculation formula of the blocking resistance is as follows:  
\[\left\{\begin{array}{l}F_{x}=T_{L}\cos\beta+T_{R}\cos\beta\\ \cos\beta=\frac{S}{\sqrt{S^{2}+\text{D}^{2}}}\end{array}\right. \tag{1}\]  
where \(F_{x}\) refers to the arresting force acting on the carrier aircraft; \(T_{L}\) refers to the pulling force of the left arresting cable; \(T_{R}\) refers to the pulling force of the right arresting cable; \(\beta\) refers to the angle between the line connecting the hook cable engagement point and the pulley, and the heading; \(S\) refers to the displacement of the carrier aircraft; and D refers to half of the distance between the two pulleys on the deck.  
In the case of yaw arrest, taking the right yaw as an example, the force of yaw arrest of the carrier aircraft is as shown in Figure 3.  
According to Figure 3, the calculation formula of blocking resistance is as follows:  
\[\left\{\begin{array}{l}F_{x}=T_{L}\cos\varphi_{L}+T_{R}\cos\varphi_{R}\\ \varphi_{L}=\theta_{L}-\varphi,\varphi_{R}=\theta_{R}+\varphi\\ \cos\theta_{L}=\frac{S\cos\varphi}{\sqrt{\left(D+S\sin\varphi\right)^{2}+ \left(S\cos\varphi\right)^{2}}}\\ \cos\theta_{R}=\frac{S\cos\varphi}{\sqrt{\left(D-S\sin\varphi\right)^{2}+ \left(S\cos\varphi\right)^{2}}}\end{array}\right. \tag{2}\]  
Figure 3: Schematic diagram of yaw arrest.  
Figure 2: Schematic diagram of arresting force.  
where \(\theta_{L}\) is the angle between the left arresting cable and the longitudinal direction, \(\theta_{R}\) is the angle between the right arresting cable and the longitudinal direction, \(\varphi_{L}\) is the angle between the left arresting cable and the heading, and \(\varphi_{R}\) is the angle between the right arresting cable and the heading, and \(\varphi_{R}\) is the angle between the right arresting cable and the heading.