esa-sceva/satcom-chunk-collection huggingface dataset:
# The Design of a Long-Distance Signal Transmission System in a Bistatic RCS Testing System
## 2 System Principle and Simulation  
The entire testing system is an inter-pulse frequency-hopping radar, and its operating principle is illustrated in Figure 2. A local oscillator signal provides the time base for two agile frequency signal sources, ensuring time synchronization between them. Agile frequency signal source 1 generates a signal \(x(t)\) with a frequency \(f_{1}=f_{i}\), which is split into two paths. One path is used as the transmission signal, which, after modulation, amplitude control, and power amplification, is transmitted towards the target by the antenna. The other path serves as the intermediate frequency (IF) for down-conversion to obtain the signal \(x_{ref}(t)\). The signal \(z(t)\) generated by agile frequency signal source 2, with a frequency \(f_{2}=f_{i}+f_{I}\), is also split into two paths. One path is used as the reference local oscillator and mixed with the processed echo signal \(y(t)\) to obtain signal \(y^{\prime}(t)\). The other path serves as the RF input for down-conversion to obtain the signal \(x_{ref}(t)\). Thus, the frequency of signal \(x_{ref}(t)\) should be \(f_{2}-f_{1}=f_{I}\). Finally, the signal \(x_{ref}(t)\) this time acts as the IF, and the other signal, \(y^{\prime}(t)\), undergoes a series of processes, including sampling, digital phase locking, quadrature digital down-conversion, and baseband signal post-processing. This process allows one to obtain various measurements, such as the target's RCS, one-dimensional image, and two-dimensional image.  
Additionally, the signal transmission system can theoretically be connected at any point in the entire testing system to increase the extent of signal transmission. A schematic diagram of this system is shown in Figure 3.  
The modulated signal, \(S_{r}(t)\), is input into an electro-optical modulator, where it modulates the optical carrier signal, \(E_{in}(t)\), generated within the modulator, forming the modulated optical signal, \(S_{m}(t)\). After long-distance optical fiber transmission, \(S_{m}(t)\) undergoes a phase shift and attenuation, resulting in the optical signal \(S_{c}(t)\). \(S_{c}(t)\) then enters a photodetector and is converted back into the electrical signal, \(S_{r}^{\prime}(t)\).
## 3 Results
### Variation in Fungal and Bacterial Communities in Paddy Fields of KA and NKA  
PCoA analysis based on Bray-Curtis distances revealed that 50.56% and 13.56% of the fungal community variation was explained by axes 1 and 2, respectively [Figure 4a], while 66.78% and 18.79% of bacterial community variation was explained by axes 1 and 2 [Figure 4b]. Bacterial and fungal communities generally segregated across the ordinations, indicating clear microbial differences between the KA and the NKA.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{**Site**} & \multirow{2}{*}{
\begin{tabular}{c} **pH** \\ **(H2O)** \\ \end{tabular} } & **SOC** & **DOC** & **TN** & **AN** & **TP** & **AP** & **C/N** & **CEC** & **Ca\({}^{2*}\)** & **Mg\({}^{2*}\)** \\  & & **(g\(\cdot\)kg\({}^{-1}\) & **/g\(\cdot\)kg\({}^{-1}\)** & **/g\(\cdot\)kg\({}^{-1}\)** & **/mg\(\cdot\)kg\({}^{-1}\)** & **/g\(\cdot\)kg\({}^{-1}\)** & **/mg\(\cdot\)kg\({}^{-1}\)**Figure 1: Situ soil respiration and cumulative CO\({}_{2}\) emissions of paddy fields. Note: Different lowercase letters in the same date indicate statistically significant differences between the KA and NKA (\(p<0.05\)); KA, karst areas; NKA, non-karst areas.  
Figure 2: The abundance of soil bacteria (**a**) and fungi (**b**) based on real-time PCR during rice growth in the KA and NKA. Note: Different lowercase letters in the same date indicate statistically significant differences between KA and NKA (\(p<0.05\)); KA, karst areas; NKA, non-karst areas.  
Figure 4: Principal coordinates analysis (PCoA) ordination of fungal (**a**) and bacterial (**b**) community variation based on Bray-Curtis distances; The relative abundances of the soil fungal (**c**) and bacterial (**d**) community at Class level. Note: KA, karst areas; NKA, non-karst areas.  
Figure 3: (**a**) The F/B during rice growth; (**b**) Correlation between soil respiration and the F/B. Note: Different lowercase letters in the same date indicate statistically significant differences between KA, and NKA (\(p<0.05\)); KA, karst areas; NKA, non-karst areas; F/B, ratio of fungi to bacteria.  
At the class level, fungi and bacteria had a total of 13 and 20 dominant classes, respectively [Figure 4c,d]. The average relative abundances of fungal Glomeromycesets, Chytridiomycetes and Exobasidiomycetes (0.91%, 0.98% and 0.23%) in KA were significantly higher than those in NKA (0.47%, 0.28% and 0.04%). The average relative abundances of bacterial \(\beta\)-proteobacteria, Acidobacteria Gp6, \(\gamma\)-proteobacteria, Anaerobic, Acidobacteria Gp4, Sphingobacteria and Gemmatimonadetes (10.32%, 9.88%, 3.74%, 3.54%, 3.13%, 2.76% and 1.38%) in KA were significantly higher than those (9.07%, 4.54%, 3.18%, 1.78%, 1.01%, 1.10% and 0.73%). The above results showed that there was a significantly difference of microorganisms between KA and NKA.
## 2 System Model  
### Network Model  
In this study, we examine a WSN consisting of \(K\) individual sensor nodes, which are randomly deployed. Each sensor node in this network is equipped with components enabling sensing, computational processing, and wireless communication functionalities. The network architecture includes a sink node located at the edge of the deployment area. Data transmission within this WSN is governed by the LEACH protocol which facilitates efficient data transmission through dynamic clustering and rotating CHs. The cluster's channel model is presumed to adhere to a free space propagation model. Additionally, the channels between CHs and the sink node are multi-path fading channels. The sink plays a role in aggregating data collected from the dispersed sensor nodes.
## 3 Modelling and Implementation of the Simulation Prototype
### Model Implementation Using Modelica  
This section contains a synthesized description of the implementation of the previously presented SysML meta-model in the Modelica language using mainly textual code to define classes and connectors with sufficient detail for generating executable simulation models. Modelica also supports the graphical appearance definition of components using annotations. This resource provides a powerful mechanism to build complex objects of study, as in the case of the MAS, whose OpenModelica graphical view is shown in Figure 7. OpenModelica software has been used to carry out both the implementation of the reusable libraries expressed with the Modelica language and the construction and execution of the simulation models.  
As mentioned before, the meta-model establishes a simulation model of the logistic flow which is later specialized to include the simulation of the quality characteristics flow. In this way, two Modelica packages are modelled: logistic flow simulation (LFS) and variation flow simulation (VFS), developed for the linear MAS described in the previous section. Figure 7 is an OpenModelica screenshot showing the structure of the libraries (packages) and a diagram view of an instantiated model. As can be  
Figure 5: State machine diagram for AssemblyProcess block.  
Figure 6: Block definition diagram of the analysis context model of the assembly line domain.  
appreciated in the figure, different types of Modelica classes have been modelled, mainly connectors, models, and functions. Some of these classes are discussed in greater detail below, but many others cannot be described in detail due to space reasons.
# A Review of Artificial Intelligence in Enhancing Architectural Design Efficiency
## 2 Materials and Methods
### Retrieval Process  
In the first phase of this review, diverse sources of research (conference papers, books, research papers, journal articles, and other literature reviews) are considered. The search terms are defined and expanded according to the article's topic: \"AI\", \"Artificial Intelligence-generated Content (AIGC)\", \"Parameterization\", \"Human-machine collaboration\", \"Building energy consumption\", and \"Architectural design\". The research field covers computer, engineering, environment, energy, and building disciplines. The search syntax is different for different databases. In the databases of Science Direct, Web of science, and Scopus, a fuzzy search was performed using titles and keywords that the literature might use. In the CNKI database, keywords and indexes are used to search.  
The study protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Program Guidelines [16], consisting several stages: (a) identifying the publication, (b) screening the publication, (c) assessing the eligibility of the publication against predefined criteria, and (d) conducting a synthesis and meta-analysis. We use inclusion and exclusion criteria in order to select papers to analyze:  
1. Research papers on the application of AIGC, machine learning, AI in the fields of parameterization, energy consumption simulation, human-machine collaboration, etc.
2. Exclude papers that study the application of AI in computer, programming, clinical medicine, agriculture, and other fields.
3. This study takes the influence of AI on architectural design efficiency as the main research topic.
4. The study focuses on the feasibility and future potential of the technology to draw conclusions.
# A Study on \(H_{\infty}\)-Fuzzy Controller for a Non-Linear Wind Turbine with Uncertainty
## 3 Control Algorithms  
### Model Linearization  
To design the \(H_{\infty}\) controller, the target wind turbine model was modeled and linearized using Bladed (Ver. 4.11, DNV, Oslo, Norway), a wind turbine commercial aero-elastic code for dynamic simulations with a controller in turbulent wind conditions. In addition, to implement the uncertainty to the model, a wind speed perturbation of 0.1 m/s, a blade pitch of 0.5 deg, and a generator torque perturbation of 0.01 Nm were applied.  
The state vectors \(x\) included each state variable of the tower with six eigenmodes, each blade variable with four eigenmodes, and state variables of the blade pitch actuator, generator, and drivetrain. The output vectors \(y\) included generator speed, generator torque, rotor azimuth angle, blade pitch angle, nacelle acceleration, blade load, and tower load, each of which can be used to confirm the operation characteristics of the wind turbine. The input vectors \(u\) included blade pitch angle, generator torque, and wind speed (the linearization model information is summarized in Table 1 of Appendix A). Therefore, the state equation of the target wind turbine was expressed as:  
\[\dot{x}=A_{47\times 47}x+B_{47\times 3}u \tag{1}\]  
The state matrix \(A\) had 47 state vectors, resulting in dimensions of 47 by 47. The input matrix \(B\), on the other hand, had a dimension of 47 by 3 due to its three input variables. The output matrix \(C\) consisted of 14 output vectors, making its dimension 14 by 47. Finally, the direct transmission matrix \(D\) had a dimension of 14 by 3. The output equation of the target wind turbine was expressed as:  
\[y=C_{14\times 47}x+D_{14\times 3}u \tag{2}\]  
Linearization models were constructed in the wind speed region rate lower than (4.0 m/s), the rated wind speed region (5.5 m/s), and wind speed region higher than (8.0 m/s), respectively, in order to respond to the non-linear effect of wind turbines. Figure 1 shows the frequency response functions of the torque and pitch loop gain with respect to generator speed in each wind speed region. As can be seen in Figure 1, the fuzzy inference-based \(H_{\infty}\)-fuzzy control algorithm must be designed to combine linearization models corresponding to different wind speed regions. This will enable us to consider the nonlinearity effects of wind turbines with respect to wind speed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specifications** & **Values** \\ \hline Rated Power & 40 W \\ Rated Rotor Speed & 678 rpm \\ Rated Generator Torque & 0.04 Nm \\ Gear Ratio & 14 \\ Rotor Diameter & 1.1 m \\ Hub Height & 0.9 m \\ Cut-in/Rated/Cut-out Wind Speed & 3, 5.5, 9 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: General specifications of a target wind turbine.
# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 2 Materials and Methods
### Planar Target-Based IntrinsicParameters Determination
Considering all control points and all target poses, a nonlinear problem is eventually obtained by using \(L_{2}\) norm as a loss metric of the reprojection errors as follows:  
\[\underset{\{t,\mathbf{\epsilon},k_{1},k_{2}\},\{\mathbf{R}_{j},\ \mathbf{t}_{j}\},\{ \mathbf{P}_{i}\}}{\text{argmin}}\sum_{j=1}^{N}\sum_{i=1}^{M}\left\|\epsilon _{ij}\big{(}\mathbf{f},\mathbf{c},k_{1},k_{2},\mathbf{R}_{j},\ \mathbf{t}_{j},\mathbf{P}_{i} \big{)}\right\|_{2}^{2}. \tag{6}\]  
By minimizing above nonlinear model, the optimal intrinsic parameters \(\mathbf{f},\mathbf{c},k_{1},k_{2}\) can be obtained. For intrinsic parameters, they are independent parameters for each camera and can, therefore, be assumed to be unchanged through mechanical fixation [37]. The relevant algorithm is given in Algorithm 1. ```
Input: images of the target under different positions by moving target: \(I_{j}\). Output: intrinsic parameters: \(f_{x},f_{y},c_{x},c_{y},k_{1},k_{2}\). Initially intrinsic parameters estimation
1. Detect the control points in a series of checkerboard images with different positions. 2. Estimate the intrinsic parameters \(\big{(}f_{x},f_{y},c_{x},c_{y}\big{)}\) using the closed-form solution using [PERSON]'s classical calibration method [38]. 3. Estimate the radial distortion coefficients \((k_{1},k_{2})\) by linear least-squares using Equation (4). Refine intrinsic parameters

Refine all intrinsic parameters by minimizing Equation (6). ```  
**Algorithm 1** Determining intrinsic parameters from planar calibration target. There is one thing to note here. Lens distortion cannot be ignored in practical application scenarios. Therefore, before calculating the essential matrix and applying the bundle adjustment method in the following steps, the coordinates of the corresponding points should be corrected according to the distortion coefficients obtained through calibration [24].
## 3 Challenges and Trends in Wireless Sensor Nodes
### Reconfigurable Acquisition Boards  
As [39] noted, a lot of sensors on the market are coupled with proprietary interfaces that can only be connected to specific data-loggers. Indeed, several sensor interfaces exist, both open and proprietary, and the lack of reconfigurability in sensor nodes and data-loggers undermines the use of a wide range of specialized sensors in a common platform [40].  
However, commercial nodes exist which support digital buses such as SPI and I\({}^{2}\)C [22] via software. Nevertheless, substitution and connection of new sensors would require firmware recompiling and reprogramming. Hence, sensor substitution and maintaining would be an arduous task, given that some sensor networks consist of hundreds to thousands of nodes. In the case of FPGAs, using partial reconfiguration, several sensor interfaces can be supported and reconfigured remotely, in order to substitute a sensor without disrupting the node's connectivity. In this respect, [PERSON] _et al_. [41,42] proposed the use of a low-cost FPGA (Spartan-3, XC3S200), in order to support several sensor interfaces such as PWM, I\({}^{2}\)C and ADC/DCA controllers. Such interfaces were implemented on a separate library with an interface based on the IEEE 1451 standard [31], which has rather limited partial reconfiguration support. The platform runs at 3 V power supply and consumes 60 mW in standby mode, 150 mW during reconfiguration and 240 mW in transmission. However, this implementation contains several shortcomings. The platform is limited in terms of reconfiguration since only difference-based partial reconfiguration is supported, where, through the comparison of two bitstreams, a third one is generated containing the differences of the columns among the two. Moreover, the FPGA reconfiguration is performed via JTAG, which is slower due to its serial nature. However, in [43], as the authors of the PowWow sensor node did (Section 3.5), the authors proposed an alternative processing board based on the MSP430 microcontroller and the Actel IGLOO FPGA, consequently sacrificing reconfiguration capabilities to attain a better power consumption.  
To conclude, modern partial reconfiguration technology offers more possibilities in terms of programming area flexibility (Virtex 4-7). Due to the high cost of these platforms, this has not been exploited at the time of writing in order to support multiple interfaces.
# Early Gapping and Platoon Merging Strategies for Autonomous Vehicles using Local Controllers
## 2 Methods
### Simulation Parameters  
To investigate these profiles, we simulated a road section with a line of vehicles, such that each vehicle is equipped with the parameters listed in Table 1.  
The masses and lengths of each vehicle were randomly determined. The safe time gap was assumed to be \(T_{g}\geq T_{S}\), \(T_{S}=1\) s for all vehicles [27; 28; 29]. All vehicles entered the road at the same initial speed \(v_{s}=100\) km/h. Inlet vehicle density was introduced based on an initial gap in accordance with the time gap at the entrance \(T_{g}(x=0)\), when \(g_{i}(t=0)=v_{s}T_{g}(x=0)\). All vehicles were given the same maximal acceleration of \(3.4\) m/s\({}^{2}\), as assumed in [30; 31; 32; 33], and the same maximal deceleration of \(6.86\) m/s\({}^{2}\)[30; 34]. The scenarios simulated 10 min of traffic with up to 500 vehicles driving. For clarity, we used \(\%d_{acc}\) and \(\%d_{stb}\), which are the _relative_ portions of the maneuvering and stabilization sections from the total preparation section, respectively.  
The simulation runs included 10 values of \(g_{merge}\) (taken from 63 m to 153 m), 9 merging positions \(x_{merge}\) (0.2 km to 1.8 km from the beginning of the road), 10 maneuvering ranges (\(\%d_{acc}=10\)-\(100\%\) of \(x_{merge}\)), 2 profiles (linear and error-function), 6 values of \(v_{gapping}\) (from 75 km/h to 100 km/h), 13 inlet densities (\(T_{g}(x=0)=1\)-\(2.2\) s) and 36 combinations of main and merging lane platoon sizes (1-35 vehicles in platoon) with a preference to the main lane. There were a total of about 140,000 different cases, each simulating a 10 min drive of up to 1000 vehicles in the main lane and an unlimited supply of continuous vehicles flowing in the merging lane (at constant density of (\(T_{g}(x=0)=1\) s).  
To measure the effectiveness of the approach with regard to the main lane flow rate, we introduced a parameter \(P_{merging}\) for the merging success, as follows. For each case, we counted the number of merges that took place and calculated the percentage of new vehicles merging into the road section out of the number of vehicles in the main lane:  
\[\textbf{{P}}_{merging}\triangleq\frac{\textbf{{Merging}}\textbf{{V}}_{ \textbf{{V}}}}{\textbf{{P}}\textbf{{V}}_{\textbf{{V}}}} \tag{8}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Time step \(\Delta t\) & 0.1 & s \\ \hline Road length \(L\) & 2 & km \\ \hline Maximal speed & 120 & km/h \\ \hline Maximal acceleration \(a_{max}\) & 3.4 & m/s\({}^{2}\) \\ \hline Maximal deceleration \(d_{max}\) & 6.86 & m/s\({}^{2}\) \\ \hline Vehicle length & 3–6 & m \\ \hline Time gap \(T_{g}\) & 1–2.2 & s \\ \hline Proportional coeff. \(K_{P}\) & 0.278 & 1/s\({}^{2}\) \\ \hline Differential coeff. \(K_{D}\) & 1.667 & 1/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter list.
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants
### Nigella sativa L.
#### 3.3.3 Antibacterial Activity  
According to the literature data, black cumin oil has antibacterial effects against _Escherichia coli_[94; 95] and _Pseudomonas aeruginosa_[94]. [PERSON] et al.'s [96] research proved that black cumin essential oil and its active compounds, i.e., thymoquinone and carvacrol, show antibacterial activity against methicillin-susceptible and methicillin-resistant _Staphylococcus aureus_ strains. In the case of methicillin-resistant _Staphylococcus aureus_, the antibacterial action of extracts derived from _N. sativa_ seeds was also confirmed [97; 98]. [PERSON] et al. [97] noted that antibacterial force was related to the higher content of thymoquinone in the extract. [PERSON] et al. [99] conducted a detailed phytochemical analysis of black cumin seeds and evaluated their antimicrobial activity. A diverse array of phytochemicals was identified across various extracts utilizing different solvents (petroleum spirit, ethyl acetate, methanol, and water). Alkaloids, steroids, and terpenoids were present in all extracts. Flavonoids, phenols, and tannins were exclusively detected in the methanolic and aqueous extracts. Furthermore, cardiac glycosides were exclusively found in the methanolic extract, while saponins were solely present in the aqueous extract. [PERSON] et al. emphasize that the antibacterial activity of _N. sativa_ may be ascribed to the content of various phytonutrients and their interactions [99]. Different black cumin seed extracts showed significant activity against various bacterial strains, including _Staphylococcus aureus_ and _Escherichia coli_[99].