esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Dilution of OC with Inert Bed Material
Figure 7: Simulation results for CLG operation through OC dilution with inert SiO\({}_{2}\) sand. OC-to-fuel ratio as a function of the inert concentration of the circulating solid mixture (**a**). Fuel reactor temperature (**b**), relative net process heat (**c**), and cold gas efficiency (**d**) for different values of \(\phi\) (\(\lambda\) = 1.2, \(\dot{m}_{\text{OC}}+\dot{m}_{\text{SiO2}}=\textit{const}\)). Despite the presented advantages, [PERSON] et al. (2018) found that, albeit slightly reducing tar loads, the addition of an active OC (limenite) to an inert circulating bed material in a dual-fluidized bed gasifier (for \(\phi<0.2\)), entails a continuous drop in cold gas efficiency. This was explained by the fact that ilmenite addition does not enhance char conversion significantly, while its presence leads to a partial oxidation of the product gas. On the other hand, [PERSON] et al. (2018) found that dilution of an active OC bed with up to 90% of an inert material does not entail visible enhancements in the cold gas efficiency of the CLG process, while it has a visible negative impact on carbon conversion. This shows that the mixing of an inert and an active OC material can have different effects on the process depending on the governing boundary conditions. Another drawback of this approach is that, albeit the addition of solids allows for an adjustment of \(\phi\) during operation, it leads to a large system inertia, making it an arduous task to quickly react to disturbances. Moreover, a fraction of the solid material has to be removed from the system for ash removal in a continuously operated CLG unit. Economic considerations require a separation of these materials for further processing, recycling, and disposal. Clearly, the presence of a third component (i.e., sand, olivine) further complicates this task. Lastly, it is known that the operation of a fluidized bed with multiple bed materials of different characteristics brings about additional challenges in terms of material fluidization, entrainment, and attrition, as well as bed segregation ([PERSON], 2018). Due to these reasons it was also suggested to employ materials of a low oxygen transport capability (\(R_{O}\)), such as LD-slag, containing a large inactive fraction not participating in the oxygen transport, which fulfills the purpose of the inert heat carrier ([PERSON] et al., 2018). Through this, oxygen carrier circulation rates providing sufficient heat transport between the reactors can be targeted, without obtaining OC-to-fuel equivalence ratios above unity. Yet, for this approach the main challenge is finding suitable OC materials exhibiting an oxygen transport capability in the desired range, high activity towards hydrocarbon conversion, and good chemical and mechanical stability.
# Simulation of Sowing Precision in Laboratory Conditions
## 2 Materials and Methods  
In order to determine the influence of the degree of soil grinding that occurs under operating conditions, it is necessary to know the variation spectrum of the vibration induced in the mechanical structures of precision planners at contact with the ground during movement on different work plots with a different degree of seedbed processing.  
The experimental research was performed on an experimental model of a precision planter, SEMA 8, with pneumatic seed meters, vertical discs with holes, lamellar singular, and 8 row units.  
Each row unit consists of the following main parts: hopper (seed tank), seed meter, seed transport tube, furrow opener, seed cover working part, and compaction wheel (Figure 1).  
The sowing precision was determined, under operating and laboratory conditions, for three row units of the studied planter (coded SO, SV, BV), under the same conditions as those in which the field sowing work was carried out (corn crop). Romania, being one of the largest corn producers in the European Union, with approximately 17 million tons produced in 2019, had a sowing rate of 50,000 pl\(\cdot\)ha\({}^{-1}\), and working speeds of 4, 6, and 8 km\(\cdot\)h\({}^{-1}\) as normal working conditions [59].  
These tests were necessary to establish the difference between the sowing precision determined when stationary, as in [60], on the stand, and the actual precision when simulating in laboratory, on the same stand, the actual operating conditions determined in the field.  
By moving the planter, the support wheels, which are also drive wheels, put into operation a common shaft which, by means of a gearbox, actuates the drive shaft that runs the seed meters.  
The vacuum generator creates the depression required for the operation of pneumatic seed meters. Actuating the distribution discs of the row units, the seeds are placed at equidistant distances in the furrow.  
The compaction and level wheel is adjusted to ensure the working depth required by the agrotechnical norms.  
Each hole drives a seed, as a result of the depression that is created near the hole of the seed meter's distributing disc.  
The evacuation of the seeds from the device is done under the action of their own weight, when the holes to which the seeds have adhered come out of the area where the depression is created.  
Figure 2 shows the scheme of the studied row unit's seed meter.  
Figure 1: Scheme of the studied row unit: (1) frame; (2) springs; (3) seed supply box; (4) seed meter; (5) vacuum channel flexible tube; (6) soil compaction wheel; and (7) furrow opener.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Receiver** & **Variant** & \(\text{HPE}_{\text{mean}}\) & \(\text{HPE}_{\text{std}}\) & \(\text{HPE}_{\text{max}}\) & \(\text{VPE}_{\text{mean}}\) & \(\text{VPE}_{\text{std}}\) & \(\text{VPE}_{\text{max}}\) \\ \hline \multirow{8}{*}{**Xiaomi Mi8**} & W: SNR & 2.59 & 2.24 & 33.26 & 3.65 & 3.25 & 30.83 \\  & Original EGNOS & 2.24 & 1.95 & 30.62 & 3.27 & 2.78 & 24.48 \\  & Iono correction off & 2.64 & 2.25 & 30.68 & 5.00 & 3.69 & 32.10 \\  & Fast correction off & 2.80 & 2.31 & 32.88 & 4.18 & 3.54 & 30.51 \\  & Slow correction off & 3.09 & 2.57 & 43.93 & 3.66 & 3.29 & 39.03 \\  & RRC off & 2.59 & 2.22 & 33.49 & 3.70 & 3.25 & 30.84 \\  & Fast and slow correction off & 2.80 & 2.45 & 38.97 & 3.73 & 3.35 & 38.70 \\  & Fast, RRC, slow correction off & 2.80 & 2.45 & 38.97 & 3.73 & 3.35 & 38.70 \\ \hline \multirow{8}{*}{**Septentrio**} & IN: SNR & 0.62 & 0.37 & 3.81 & 2.15 & 1.29 & 5.06 \\  & Original EGNOS & 0.39 & 0.18 & 1.48 & 0.45 & 0.36 & 2.13 \\ \cline{1-1}  & Iono correction off & 0.73 & 0.26 & 1.70 & 2.87 & 0.67 & 5.25 \\ \cline{1-1}  & Fast correction off & 1.30 & 0.61 & 3.53 & 1.12 & 0.76 & 3.47 \\ \cline{1-1}  & Slow correction off & 1.17 & 0.44 & 2.50 & 1.43 & 0.76 & 4.09 \\ \cline{1-1}  & RRC off & 0.39 & 0.18 & 1.24 & 0.45 & 0.36 & 1.97 \\ \cline{1-1}  & Fast and slow correction off & 0.74 & 0.42 & 1.81 & 0.73 & 0.56 & 2.37 \\ \cline{1-1}  & Fast, RRC, slow correction off & 0.74 & 0.42 & 1.81 & 0.73 & 0.56 & 2.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Numerical values of the accuracy analysis performed for GPS/EGNOS positioning with a Xiaomi Mi8 smartphone and Sepentrio AsteRx2 receiver in different variants. All values are expressed in meters. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Receiver** & **Positioning Variant** & \(\text{HPE}_{\text{mean}}\) & \(\text{HPE}_{\text{std}}\) & \(\text{HPE}_{\text{max}}\) & \(\text{VPE}_{\text{mean}}\) & \(\text{VPE}_{\text{std}}\) & \(\text{VPE}_{\text{max}}\) \\ \hline \multirow{8}{*}{**Xiaomi Mi8**} & GPS autonomous (weight: SNR) & 2.72 & 2.18 & 53.56 & 3.90 & 3.82 & 88.14 \\  & GPS/EGNOS, smoothing window: & & & & & & \\ \cline{1-1}  & 20 s; steady state: 60 s (weight: SNR) & 2.59 & 2.24 & 33.26 & 3.65 & 3.25 & 30.83 \\ \cline{1-1}  & GPS/EGNOS, smoothing window: & & & & & & \\ \cline{1-1}  & 20 s; steady state: 60 s (standard & 2.24 & 1.95 & 30.62 & 3.27 & 2.78 & 24.48 \\ \cline{1-1}  & EGNOS weighting model) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Numerical values of the accuracy analysis performed for positioning with the Xiaomi Mi8 smartphone: autonomous GPS (SNR-dependent observation weighting); GPS/EGNOS with a smoothing window of 20 s and a steady state of 60 s (SNR-dependent observation weighting); and GPS/EGNOS with a smoothing window of 20 s and a steady state of 60 s (standard EGNOS weighting model). All values are expressed in meters.
## 3 Simulation Environment and Approaches
### Model in the Loop
#### 3.1.1 Sun Sensor Model  
The Simulink implementation of the solar sensor model for the control system is based on the addition of six SLCD-61N8 photodiodes. These sensors were modeled for implementation in the verification framework. The objective was to simulate an environment, accounting for the response of electronic components of interest, such as those receiving external signals. Implementing the electronic circuits with the solar sensors included allows for a simulated environment with additional considerations, such as noise and typical errors in the signals obtained, which are later processed by the next control stages.  
These photodiodes simulate current signals in a controlled manner, which are given by the equation \(I=R\cdot A\cdot\beta\) where \(R\) is the incident solar irradiance in the area, \(A\) is the active area, and \(\beta\) is the chosen sensitivity of the photodiode. The electronic conditioning circuit was designed to convert the input signal provided by the photodiode (given by the output current \(I\)) into an output voltage signal V sun sensor proportional to the aforementioned input. The model chosen for the design was based on the topology of the instrumentation amplifier circuit [44]. This was carried out to condition the signal to a voltage output. In addition, a low-pass filter [45] was added for output stability, high-frequency signal filtering, and electrical isolation for each photodiode. To obtain a voltage output relative to a current input, the traditional instrumentation amplifier topology was adapted to one containing two trans-impedance amplifiers and a differential amplifier (Figure 5). This arrangement allows for a linear relationship between the circuit voltage output and the photodiode input current. The gain is mainly determined by the feedback resistor ratio and the resistors \(R_{4}\) and \(R_{3}\) as follows:  
\[V_{out}=2\cdot R_{F}\cdot(\frac{R_{4}}{R_{3}})\cdot I_{in} \tag{26}\]  
The circuit design aspects for achieving the correct gain in the impedance circuit were analyzed based on the maximum short-circuit current of the SLCD-61N8 photodiode. The typical current for this photodiode is 170 \(\mu\)A at an irradiance of 25 mW/cm\({}^{2}\). Therefore, for a total solar irradiance of 136.7 mW/cm\({}^{2}\), a maximum current of 929 \(\mu\)[A] is obtained through linear ratio calculation.  
The values assigned to \(R_{F}\), \(R_{3}\), and \(R_{4}\) in the Simulink model are 390 \([\Omega]\), 2.7 k\([\Omega]\), and 12 k\([\Omega]\), respectively. It is important to note that both resistors \(R_{3}\) and \(R_{4}\) must match \(R_{1}\) and \(R_{2}\) to ensure a correct output voltage of approximately 3.25 [V], with a maximum current flow of 929 \(\mu\)A. The signal from each sun sensor is processed by the previously explained circuit, and the circuit's output reveals the output voltage of the sun sensor model. Additionally, the implementation block of one of the six sun sensors is depicted in Figure 5.
# Software Defined Networking Architecture for Energy Transaction in Smart Microgrid Systems
## 7 Conclusions  
Transactive energy (TE) consists of economic and control mechanisms to balance the supply and demand of energy on an electrical network. The link between economics and energy is transactive, with various participants in the transactive energy process. These participants include consumers, prosumers, utility networks, and distributed energy sources such as SMGs. The participants of transactive energy use different types of markets, including the forward market for future delivery of energy in a pre-paid energy market concept and the spot market for real-time energy delivery in a pay-per-use commercial model. Literature also confirms that the primary communication is between local participants of the TE process. Therefore, there is a requirement for a reliable, flexible, and secure network for transactive energy.  
There is also a research gap in the literature on transactive energy for reliability, flexibility, and security in traditional IP network infrastructure. Software-defined networking (SDN) emerged to meet the requirements of complex networks. However, the research found that application-aware SDN architectures are designed to meet the specific application's requirements. This revelation confirms the need to develop an SDN network that meets the requirements for transactive energy in smart microgrids. In addition, the optimized SDN architecture is required to improve the network's reliability, flexibility, and security.
# A Review on Decentralized Finance Ecosystems
## 4 The Taxonomy
### Low Cost  
DeFi service providers execute operations at a lower cost than TradFi, primarily due to the absence of fees charged by banks and other financial entities for service usage, enabling investors to respond to changing environmental circumstances more affordably.  
Moreover, the decentralized and automated nature of DeFi operations leads to significantly narrower profit margins than those of TradFi institutions, resulting in advantageous rates for borrowers and heightened operational efficiency. These cost efficiencies stem from the automated and unregulated environment of DeFi platforms, resulting in lower marginal costs when contrasted with banks and nonbank entities across advanced and emerging market economies. DeFi provides cost-effective services through smart contracts, eliminating the necessity of intermediaries such as financial institutions and banks. Due to the automation of repetitive and low-value tasks, they can often offer lower fees while maintaining higher efficiency [18]. The transparent nature of DeFi reduces the need for external audits and verifications, typically associated with expenses in traditional financial services. DeFi service providers enable investors to adjust to evolving conditions efficiently while minimizing costs through task automation and implementing lower fees [18].  
High liquidity pertains to the seamless exchangeability of tokens or cryptocurrencies with other tokens in DeFi. Liquidity is paramount in DeFi, facilitating user transactions on decentralized exchanges without relying on centralized market makers. At the core of the DeFi ecosystem are liquidity pools, which are collaborative reserves of cryptocurrencies or tokens held in smart contracts. These pools empower users to contribute asset liquidity and earn rewards through transaction fees. Liquidity is indispensable for DeFi, serving as a vital catalyst for trading and eliminating impediments to the fluidity of digital assets [13; 98].  
High liquidity is essential for companies to meet their short-term debt obligations promptly. Within the DeFi ecosystem, liquidity pools and yield farms significantly attract investors with the promise of high returns over a short period. These mechanisms serve as powerful incentives for various projects, drawing attention and participation from investors. As a result of these promotional efforts, DeFi experiences high liquidity levels. The availability of ample liquidity facilitates smooth and efficient transactions within the DeFi space, enabling users to access and exchange assets efficiently and enhancing the overall functionality and appeal of the DeFi ecosystem [13].
## 6 Evaluation Results
### Results of the Applied TRAM Model
Consequently, starting with the covariance matrix, different models were computed using maximum likelihood robust estimates. Several iterations and modifications, as indicated by the Lagrange multiplier and Wald tests, resulted in the final model. The Wald test was used to test the need for these regressions and to suggest a more restricted model. The confirmed path model is displayed in Figure 4, with good overall fit of this model: Satorra-Bentler scaled \(\chi 2\) (13, N = 44) = 16.07, \(p\) = 0.25, CFI = 0.98, and RMSEA = 0.07 (CI90% 0.00 to 0.18). Only statistically significant effects have been included in the confirmed model. The initial hypotheses H2, H8 (although not negative but positive), H9, H10a and H13 are confirmed and ten new relationships havingas starting point the four dimensions of technology beliefs (TRI) are revealed, as described in Table 2. These new relationships are represented as HYnumber in Figure 4.
# Cybersecurity Vulnerabilities in Off-Site Construction
## 3 Importance of Cybersecurity in Manufacturing and Construction  
Off-site construction is one of the emerging alternatives to traditional construction. The literature also refers to it as modular construction, prefab construction, prefinished volumetric construction, and precast construction [25; 26]. These terms have a consensus of splitting the construction between a controlled manufacturing site and a construction site.  
Construction and manufacturing have been categorized separately for decades with overlaps that show that they depend on each [27]; however, in retrospect, these two industries are not only reliant on each other but are part of the same value chain [28]. Currently, OSC is being implemented in individual unit and multi-family housing, commercial buildings, and the public service sector, such as hospitals and schools [28]. This sector is also adopting Industry 4.0 technologies, and it is projected to advance even further in the future [21].  
The unique features of OSC involve transferring the construction elements to a controlled factory away from the construction site. This can potentially improve the safety and speed of construction and lower the waste production rates. It has been reported that OSC can potentially save between 30 to 50% of the project time due to minimized workforce movement [28]. Furthermore, a construction project on an estimated area between 10,000 to 20,000 m\({}^{2}\) can be delivered within 4 to 5 months [28].  
Currently, the full potential of OSC is still futuristic. It remains limited in its adoption because of the extended supply chain that demands more coordinated monitoring between the manufacturing and construction parties. In addition, its adoption requires a restructuring of labor assignments and management, especially during the onsite assembly of the prefabricated modules. This inevitably requires critical skills, which counters the significant advantage of cost-savings given by OSC. The major cost-cutting benefit is drastically reduced to below 5% in total labor savings coupled with a steep learning curve [28].  
However, for a fair assessment of the effectiveness of OSC over traditional construction, the period of this technology needs to be considered as well as other key performance indicators, which include the project payback period, return of investment, and labor cost [29]. As presented in this paper, the adoption of Industry 4.0 technologies must simultaneously be linked with cybersecurity upgrading and awareness, which is not currently practiced. Many organizations do not have the updated infrastructure to deal with the emergent vulnerabilities that provide room for cyber-attacks.
# Genetic Algorithms to Maximize the Relevant Mutual Information in Communication Receivers +
## 2 Preliminaries
### Genetic Algorithm Optimization
#### 3.2.2 Using Genetic Algorithms to Maximize the Preserved Relevant Information  
We propose to perform the optimization of the parameters \(\boldsymbol{\theta}=[\boldsymbol{\theta}_{0},\boldsymbol{\theta}_{1},\ldots, \boldsymbol{\theta}_{|\mathcal{T}|-1}]\) for all considered mappings and irrespective of the actually used distance function \(d(\mathbf{y},\boldsymbol{\theta}_{t})\) with a genetic algorithm. For that purpose, we initially draw a population of individuals \(\mathcal{P}=\left\{\boldsymbol{\theta}^{(0)},\boldsymbol{\theta}^{(1)},\ldots,\boldsymbol{\theta}^{(n_{\mathrm{pop}}-1)}\right\}\). As it is typically assumed in the information bottleneck setup, we assume that the joint probability distribution \(p(x,\mathbf{y})\) is known.  
For any population member \(\boldsymbol{\theta}^{(l)}\) it is then straightforward to determine the joint probability distribution \(p(x,t)\) for this particular individual as  
\[p^{(l)}(x,t)=p^{(l)}(x|t)p^{(l)}(t)=\sum_{\begin{subarray}{c}\mathbf{y}\in \mathcal{Y}:\\ l=f_{\boldsymbol{\theta}(l)}(\mathbf{y})\end{subarray}}p(x,\mathbf{y}), \tag{7}\]  
and  
\[p^{(l)}(t)=\sum_{x\in\mathcal{X}}p^{(l)}(x,t). \tag{8}\]  
These distributions directly allow us to calculate the respective preserved relevant information \(\mathrm{I}(\mathsf{X};\mathsf{T})\) for this population member according to Equation (4), that is,  
\[\mathrm{I}^{(l)}(\mathsf{X};\mathsf{T})=\sum_{x\in\mathcal{X}}\sum_{t\in \mathcal{T}}p^{(l)}(x,t)\log\frac{p^{(l)}(x,t)}{p(x)p^{(l)}(t)}. \tag{9}\]  
Note that \(0\leq\mathrm{I}^{(l)}(\mathsf{X};\mathsf{T})\leq\mathrm{I}(\mathsf{X};\mathsf{ Y})\) by definition. This allows us to use the mutual information \(\mathrm{I}^{(l)}(\mathsf{X};\mathsf{T})\) directly as fitness \(g\left(\boldsymbol{\theta}^{(l)}\right)\) of the population members \(\boldsymbol{\theta}^{(l)}\) in the generations of the genetic algorithm. The rest of the processing then just follows the standard processing of genetic algorithms using selection, genetic crossovers and mutations over the generations as described, for example, in [19; 20].  
It is very important to note that all the involved equations can be evaluated totally irrespective of the actual operations performed in the signal processing block \(f_{\boldsymbol{\theta}}(\mathbf{y})\) from Figure 2. The presented equations in fact work for all possible deterministic mappings of \(\mathbf{y}\) onto \(t\in\mathcal{T}\). The genetic algorithm just treats \(f_{\boldsymbol{\theta}}(\mathbf{y})\) as a black box. Therefore, we can just use either the exact or the approximate nearest neighbor search approaches from Sections 3.1.1-3.1.3. We can also freely decide what distance measure \(d(\mathbf{y},\boldsymbol{\theta}_{t})\) we want to use. As a result, the presented approach is very generic.