esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sample Annealing and Crystal Structure  
Annealing of the toroid was performed for obtaining improved magnetic properties. The process was performed in a graphite chamber furnace on the account of the ready availability of this facility for this study. The toroid was heated up in vacuum environment (\(\sim\)0.1 mBar) to 1150 \({}^{\circ}\)C with a heating rate of 300 K/h, kept at the target temperature for 1 h and then slowly oven-cooled. The annealing parameters were chosen as optimal parameters described in the study by [PERSON] [12]. Due to potential carbon absorption during the annealing process in the graphite chamber furnace, Atomic Emission Spectroscopy (AES) analysis of the carbon content in the printed material was conducted both before and after annealing. The spectroscopy was performed on the surface of the samples, with three tests for both one annealed and one unannealed sample. The carbon content was measured at 0.01% before the annealing and 0.03% after the annealing, suggesting absorption of carbon by the surface of the samples during annealing. The penetration depth of the absorbed carbon and its effect on the magnetic properties of printed steel is planned for a future study. Significant grain growth was observed (Figure 4b) after the annealing process: the fine-grained microstructure recrystallized into a course-grained microstructure (D\(\sim\)500 \(\upmu\)m). The as-built state shows columnar microstructure with a strong texture in the build direction resulting from epitaxial growth and steep temperature gradients in the build direction. Similar to [11], the epitaxial structure has disappeared in the annealed state; however, the coarse grains appear to have retained the elongated columnar structure in the build direction post-annealing.  
XRD (X-ray diffraction) analysis was performed to investigate the micro-phase distribution in as-built and annealed material. The XRD patterns suggest the evolution of the preferred orientation  
Figure 4: Optical micrographs of the printed material grain structure (**a**) before and (**b**) after annealing. XRD (X-ray diffraction) analysis of the printed toroid (**c**) before and (**d**) after annealing (measured from the cross-sectional plane of the toroid).  
from \(\langle 1\ 1\ 0\rangle\) to \(\langle 1\ 0\ 0\rangle\) crystallization axis (easy magnetization [21]) in parallel to the magnetization direction in the recrystallized toroid. This indicates the capacity of recrystallized laser re-melted SLM structure to exhibit beneficial grain structure for magnetic applications, prompting future research.
# How Automated Machine Learning Can Improve Business
## 4 Conclusions
Contributions AutoML can make to both theory and practice:  
(i) Theoretical Contributions: (a) Advancement of Predictive Models: AutoML democratizes access to sophisticated predictive models, allowing for the exploration of more complex theories that were previously constrained by the need for extensive manual data processing and model selection; (b) By automating the model selection and optimization process, AutoML enables the development of more accurate and reliable decision-making models, contributing to the theoretical understanding of decision science in business contexts; (c) AutoML facilitates the scaling of research experiments, allowing for the testing and validation of theoretical models across larger datasets and more diverse business scenarios, which contributes to more robust and generalizable theories; and (d) AutoML supports the integration of techniques from various disciplines such as statistics, computer science, and economics, leading to the development of more comprehensive theories that incorporate insights from multiple fields;  
(ii) Practical Contributions: (a) AutoML can streamline and accelerate the model development process, reducing the time and cost associated with manual model building and allowing businesses to deploy data-driven strategies more rapidly; (b) By providing businesses with advanced machine learning capabilities without the need for specialized expertise, AutoML enables companies to leverage data science more effectively, thereby improving their competitiveness in the market; (c) AutoML optimizes the use of data and computational resources, allowing businesses to achieve more with less. This efficiency can lead to cost savings and better allocation of resources toward other critical business functions; (d) AutoML tools lower the barrier to entry for businesses that may not have access to expert data scientists, empowering a broader range of companies to utilize machine learning for decision-making and operational improvement; and (e) By automating routine tasks, AutoML frees up data scientists and analysts to focus on more innovative aspects of business strategy, leading to the development of new products, services, and processes. AutoML has the potential to make significant contributions to both the theoretical understanding and practical application of machine learning in business, driving innovation, efficiency, and competitiveness across industries. Exploring future lines of investigation into how Automated Machine Learning (AutoML) can improve business involves identifying emerging trends, challenges, and opportunities that could shape the next phase of research and application.
# Chiral Dielectric Metasurfaces for Highly Integrated, Broadband Circularly Polarized Antenna
###### Abstract  
We report on the design of a low-profile integrated millimeter-wave antenna for efficient and broadband circularly polarized electromagnetic radiation. The designed antenna comprises a chiral dielectric metasurface built with a \(2\times 2\) arrangement of dielectric cylinders with slanted-slots at the center. A broad-beam high-gain with wide axial ratio (AR) \(<3\) dB bandwidth was reached by pairing the electric and magnetic resonances of the dielectric cylinders and the slanted slots when excited by an elliptically polarized driven-patch antenna. This electric-magnetic pairing can be tuned by varying the cylinders diameter and the tilting and rotation angles of the slanted slots. The simulation results indicate impedance-matching bandwidths up to 22.6% (25.3-31.6 GHz) with 3-dB AR bandwidths of 11.6% (26.9-30.2 GHz), which in terms of compactness (0.95\(\lambda_{0}\times\) 0.95\(\lambda_{0}\)) and performance are superior to previous antenna designs. Since the simulations were performed by assuming materials and geometries easily implementable experimentally, it is hoped that circularly polarized antennas based on chiral metasurfaces can be integrated into 5G and satellite communications.  
chirality; dielectric resonator antennas; metasurfaces 202121207113390/s21062071
# Error Detection for Arabic Text Using Neural Sequence Labeling
## 4 Implementation and Experiments
### Toolkit and Corpus Preparation  
Keras with a TensorFlow backend is used for all the experiments. Particularly, Keras version 2.2.4 and Tensorflow version 1.12.0 are used. For the use of neural networks in Keras, input sequences need to be equally long for modeling. Therefore, we pad our sentences to a uniform maximum length, which is set to 17, since this is the length of the longest sentence in the corpus. Additionally, label sequences were padded with zeros to the maximum length. The model learns that padding values carry no information.
# Application Scenarios of Digital Twins for Smart Crop Farming through Cloud-Fog-Edge Infrastructure
## 2 Background  
### Smart Crop Farming  
Agriculture plays a crucial role in today's world, as it is essential for feeding the global population. With a growing global population and limited resources [16], it is crucial to find innovative solutions to maximise crop yield and ensure food security. One such solution is the application of smart agriculture techniques, which utilise advanced technologies such as satellite imaging and remote sensing, drones and Unmanned Aerial Vehicles (UAVs), artificial intelligence, the Internet of Things (IoT), and robotics. Smart agriculture, often referred to as precision farming, digital farming, or agriculture 5.0 [4], aims to optimise agricultural practices by utilising the power of data and technology. Smart agriculture, also known as precision farming or digital farming [17] or agriculture 5.0 [4], aims to optimise the farming process by increasing accuracy and customisation for specific fields or crops. Using these technologies, farmers can collect real-time data on soil moisture, temperature, nutrient levels, and pest infestations. These data can then be analysed and used to make data-driven decisions about irrigation, fertiliser application, pest control, and crop management. By integrating smart technologies into agriculture, farmers can not only increase crop yields but also reduce resource wastage.  
Crop farming is generally complex and relies heavily on various factors such as weather conditions, soil quality, irrigation management, fertiliser and nutrient availability, and pest control. In the past, farmers had to rely on traditional methods and intuition to make informed decisions [18]. However, with the introduction of smart agriculture or smart farming, farmers now have access to real-time data and accurate weather forecasts compared to the past. This helps them make timely decisions regarding when to plant, irrigate, or harvest their crops to optimise yield. To address this challenge, farming now demands a decision support system and monitoring system [19]. This is where the IoT plays a crucial role. The IoT refers to the connection of physical objects, devices, and sensors through the internet to gather data for decision-making [20]. By implementing IoT technology in agriculture, farmers can collect data from various sensors and devices placed throughout their fields. This data can then be analysed using artificial intelligence algorithms to provide insights and recommendations for improving crop production. Other technologies, such as machine learning, blockchain, and robotics, can also be integrated into smart agriculture systems to automate tasks, reduce labour costs, and improve efficiency. Digital twin technology in agriculture is another emerging concept that holds great potential in smart agriculture. Overall, smart crop farming is transforming traditional methods of farming, bringing about a new era of efficiency and productivity in the agricultural sector.
# Model for Determining Parking Demand Using Simulation-Based Pricing
###### Abstract  
Urban traffic management faces significant challenges in balancing parking supply with user demand. This study introduces a novel parking demand model that integrates simulation-based pricing with elasticity functions derived from revealed preference data, segmented across predefined user categories, such as short-term visitors (e.g., shoppers) and monthly subscribers (e.g., commuters). Unlike previous models, this approach does not rely on survey-based inputs and explicitly accounts for both natural and chaotic demand behaviors, thereby improving forecasting accuracy under oversaturated conditions. The model supports sustainable parking management by optimizing space availability, while simultaneously increasing occupancy and enhancing revenue generation.  
demand function; parking demand simulation; key parking demand parameters; garage parking prices Article
# Triaxial Experimental Study of Zinc Contaminated Red Clay under Different Temperature Conditions
## 4 Conclusions  
In this paper, red clay soil from a foundation pit in Guilin City, Guangxi Zhuang Autonomous Region was used as the test soil, and the triaxial shear test of the red clay soil was conducted with temperature, zinc ion concentration, and surrounding pressure as variables to analyze the effects of these three variables on the deformation and strength of the local zinc-contaminated red clay soil in Guilin City. The following conclusions were obtained.  
(1) The stress-strain relationship of red clay is strain-hardened at varying temperatures, different Zn ion concentrations, and different surrounding pressures, depending on which variable is altered. In the stress-strain relationship, the deformation modulus of the red clay tends to grow rapidly with an increase of axial strain before decreasing slowly.  
(2) Temperature can change the strength of red clay. Under the same zinc contamination concentration and circumferential pressure, the red clay increases with the temperature, which boosts the thermal consolidation and changes the cohesion and internal friction angle of the red clay, resulting in a continuous increase in shear strength.  
(3) The concentration of zinc contamination can also affect the shear strength of red clay. Under the same temperature and surrounding pressure, the shear strength of red clay tends to increase and subsequently drop as the concentration of zinc contamination rises.  
(4) The surrounding pressure is also related to its shear strength. As the volume of red clay decreases in response to an increase of the surrounding pressure, the effective stress of the soil increases, hence increasing its shear strength.  
Conceptualization, J.W. and Y.S.; methodology, J.W.; validation, Y.G., S.D. ([PERSON]) and S.D. ([PERSON]); investigation, X.G.; writing--original draft preparation, J.W.; writing--review and editing, Y.S. and J.W.; supervision, [PERSON] and S.D. ([PERSON]); project administration, Y.S. All authors have read and agreed to the published version of the manuscript.  
(1) Guangxi Innovation-Driven Development Special Project \"Research and Demonstration of Key Technologies for Water Resource Utilization and Synergistic Development of Water Ecological Industry in Typical Karst Wetlands in the Lijiang River Basin\" (Guike AA20161004-1); (2) National key research and development program subject \"R&D and experimental demonstration of key technologies for water resource regulation in Karst wetlands in the Lijiang River Basin\" (2019 YFC0507502); (3) The National Natural Science Foundation of China Project \"Research on the Collapse Mechanism of Karst Water-soil Coupling in Guilin under Extreme Climate Conditions\" (41967037).  
Not applicable.  
The authors declare no conflict of interest.
## Appendix A Attitude Controller Design Based on MPC
For the control input \(\mathbf{u}_{[k+i]}\), where \(i=1,2,\ldots,N_{\text{p}}\), there are upper and lower boundary vectors \(\mathbf{u}_{\text{max}}\) and \(\mathbf{u}_{\text{min}}\):  
\[\mathbf{u}_{\text{min}}\leq\mathbf{u}_{[k+i]}\leq\mathbf{u}_{\text{max}}. \tag{109}\]  
Substituting the feedback linearization control law \(\mathbf{u}=\mathbf{M}(\mathbf{\tau},\mathbf{\omega})^{-1}(\mathbf{v}-\mathbf{N}(\mathbf{\tau},\mathbf{\omega},\mathbf{\varkappa}))\) into the above equation, it can be derived that:  
\[\mathbf{u}_{\text{min}}\leq\mathbf{M}_{[k+i-1]}^{-1}(\mathbf{v}_{[k+i]}-\mathbf{N}_{[k+i-1]}) \leq\mathbf{u}_{\text{max}}. \tag{110}\]  
Then, the boundaries of \(\mathbf{u}_{[k+i]}\) can be expressed as inequality constraints on \(\mathbf{v}_{[k+i]}\):  
\[ \begin{pmatrix}\mathbf{I}_{3}\\ -\mathbf{I}_{3}\end{pmatrix} \mathbf{v}_{[k+i]}\leq \begin{pmatrix}\mathbf{M}_{[k+i-1]}\mathbf{u }_{\text{max}}+\mathbf{N}_{[k+i-1]}\\ -(\mathbf{M}_{[k+i-1]}\mathbf{u}_{\text{min}}+\mathbf{N}_{[k+i-1]})\end{pmatrix} . \tag{111}\]  
Since \(\mathbf{v}_{[k+i]}=\mathbf{v}_{[k+i-1]}+\delta\mathbf{v}_{[k+i]}\), the Equation (111) can be written as:  
\[ \begin{pmatrix}\mathbf{I}_{3}\\ -\mathbf{I}_{3}\end{pmatrix} \delta\mathbf{v}_{[k+i]}\leq \begin{pmatrix}\mathbf{M}_{[k+i-1]} \mathbf{u}_{\text{max}}+\mathbf{N}_{[k+i-1]}-\mathbf{v}_{[k+i-1]}\\ -(\mathbf{M}_{[k+i-1]}\mathbf{u}_{\text{min}}+\mathbf{N}_{[k+i-1]}-\mathbf{v}_{[k+i-1]})\end{pmatrix} . \tag{112}\]  
Similarly, the upper and lower boundary vectors of the angular velocity rate \(\mathbf{\omega}_{[k+i]}\) can be expressed in the following form:  
\[\mathbf{\omega}_{\text{min}}\leq\mathbf{M}_{[k+i-1]}^{-1}\hat{\mathbf{\Omega}}_{[k+i]} \leq\mathbf{\omega}_{\text{max}}. \tag{113}\]  
Since \(\hat{\mathbf{\Omega}}_{[k+i]}=\mathbf{M}_{[k+i-1]}\mathbf{\omega}_{[k+i]}\), the inequality constraints on the \(\hat{\mathbf{\Omega}}_{[k+i]}\) can be derived as:  
\[ \begin{pmatrix}\mathbf{I}_{3}\\ -\mathbf{I}_{3}\end{pmatrix} \hat{\mathbf{\Omega}}_{[k+i]}\leq \begin{pmatrix}\mathbf{M}_{[k+ i-1]}\mathbf{\omega}_{\text{max}}\\ -\mathbf{M}_{[k+i-1]}\mathbf{\omega}_{\text{min}}\end{pmatrix} . \tag{114}\]  
For quadratic programming problems with inequality constraints, analytical solutions are often difficult to obtain, so numerical methods are used. Substituting Equations (108), (112) and (114) into the quadprog function, the virtual control increment sequence \(\delta V_{[k]}^{*}\) can be solved. The feedback linearized system's virtual control vector is  
\[\mathbf{v}_{[k]}= \begin{bmatrix}\mathbf{I}_{3}&\mathbf{0}_{3}&\cdots&\mathbf{0}_{3}\end{bmatrix}  \delta\mathbf{V}_{[k]}^{*}+\mathbf{v}_{[k-1]} \tag{115}\]
## 5 Implementation and Results
### Multivariable Optimization  
Since the framework is verified by the single variable case, we can use it to directly optimize all the 8 angles independently. The parameter vector is therefore \(\mathbf{p}=[\phi_{1},-\phi_{2},\phi_{3},-\phi_{4},-\phi_{5},\phi_{6},-\phi_{7},\phi _{8}]\), where \(\phi_{1-8}\) are the absolute values of the angles and the sign represent the rotation direction. The Figure 14 shows the multi-variable optimized AMS compared to the initial AMS and RMS.  
Figure 12: Comparison of RMS (red), initial AMS (cyan), and optimal AMS (green) of the single variable optimization. As can be seen, the optimal AMS largely increase yaw capability without minor sacrifice of the other two axes.  
Figure 13: Comparison of margin factor CDF of all the query direction (RMS vertices) between initial and optimal AMS. The overall margin factor of the optimized AMS is elevated compared to the initial one.  
A comparison between the single variable and the multi-variable optimized AMS are shown below. The corresponding optimal angles, optimal cost function and nonlinear constraint value are for the initial system, single-variable optimized system and multi-variable optimized system are summarized in the table below. Note that the multi-variable optimization achieves a more even distributed space between the axes, especially in the \(n_{z}-\dot{r}\) plane on the upper-right subplot of Figure 15. The multi-variable optimization is able to achieve a smaller cost function compared to the single-variable case as shown by Table 1.  
Figure 14: Comparison of optimal AMS, initial AMS, and RMS of the multivariable optimization setup.  
Figure 15: Comparison of single-variable optimized AMS and multi-variable optimized AMS.
# Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference
## 2 Controller Design
### Lateral Control
#### 2.2.3 Fuzzy Pure Pursuit with Front Axle Reference  
In this section, to improve the tracking accuracy and robustness of PPC-FAR, a fuzzy logic inference machine is adopted to calculate the appropriate \(k\) and \(l^{\prime}{}_{d}\). The input quantities of the fuzzy logic inference machine are defined as the curvature of the desired path \(\rho\) and the velocity of the driverless bus \(v\). Meanwhile, \(k\) and \(l^{\prime}{}_{d}\) are used as the output quantities. The basic domain of \(v\) is \([0,20]\), the basic domain of \(\rho\) is \([0,0.2]\), the basic domain of \(l^{\prime}{}_{d}\) is \([3,25]\), and the basic domain of \(k\) is \([0.5,1]\).  
As shown in Figure 9, the membership function is gaussmf type [6]. Both the inputs and outputs are qualitative into seven sets, denoted as NB, NM, NS, ZO, PS, PM, and PB, where NB is negative big, and PB is positive big.  
The rule bases of the fuzzy logic inference machine are established based on the trial and error in the real bus experiments. There are two fundamental principles:  
1. When \(\rho\) is small and \(v\) is large, in order to achieve a trade-off between accuracy and stability of PPC-FAR, \(k\) and \(l^{\prime}{}_{d}\) should be large.
2. When \(\rho\) is large and \(v\) is small, in order to guarantee the tracking accuracy and merge the gap between real bus and kinematic model, \(k\) and \(l^{\prime}{}_{d}\) should be small.  
The rule bases of the fuzzy logic inference machine are shown in Tables 2 and 3. Each rule in the rule base are implemented in the IF-THEN form, and Figure 10 shows the interface of the outputs.  
Figure 9: Membership functions. (**a**) Membership functions of \(v\). (**b**) Membership functions of \(\rho\). (**c**) Membership functions of \(l^{\prime}{}_{d}\). (**d**) Membership functions of \(k\).  
The center of gravity method is adopted as the defuzzification method, which is shown as Equation (10):  
\[u=\frac{\sum\limits_{i=a}^{b}u_{c}(z_{i})z_{i}}{\sum\limits_{i=a}^{b}u_{c}(z_{i})} \tag{10}\]  
where \(u\) is an accurate output value; \(z_{i}\) denotes the fuzzy inference output value; \(u_{c}(z_{i})\) is the membership value of \(z_{i}\); \(a\) and \(b\) are, respectively, the upper and lower bounds of the fuzzy set theory domain of the output variables.