esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Cost Rapid Fabrication of Conformal Liquid-Metal Patterns
## 1 Introduction  
Patterning conformal structures on flexible and stretchable substrates have many applications, such as flexible and stretchable sensors, antennas, or electronic skin. Accordingly, the implementation techniques of such devices should be done with flexible and stretchable materials [1; 2; 3; 4; 5] that are optimally compatible with low-cost rapid prototyping. Many low-cost rapid prototyping fabrication techniques have been developed to realize flexible RF devices. Spraying conductive inks have been used in flexible sensors and RF devices for various applications, such as a stretchable RF antenna [6], radio-frequency identification (RFID) antenna [7], optical antenna [8], strain sensors [8], sensors on skin [9; 10], and wearable electronics [11]. However, nanoparticle-based conductive inks can form cracks after the solvent evaporates [12; 13]. Recently, liquid metals (LM) have been used as an alternative to nanoparticle conductive inks to realize flexible electronics. Like conductive inks, a LM such as Galinstan (a eutectic alloy of gallium, indium, and tin) can be sprayed to create conductive features on flexible substrates. Using a shadow mask to pattern sprayed Galinstan [14] resulted in a minimum line width of 500 \(\upmu\)m, but the micromachined mask increased the cost of the fabrication process. A vinyl mask for rapid prototyping was limited to line structures with a minimum width of 200 \(\upmu\)m [15]. More complex LM structures were patterned using a screen mesh [16], but the resolution of patterned structures was limited to 234 \(\upmu\)m with minimum gaps of 75 \(\upmu\)m.  
In this paper, we used two types of masks to pattern LM: a UV-curable mask and a polyimide mask. A desktop digital craft cutter was used to cut an adhesive polyimide mask for patterning sprayed LM. Cutting the mask out of polyimide tape is a low-cost method that is compatible with rapid prototyping of complex structures. The polyimide masks were used in conjunction with the spraying of LM to achieve a minimum feature width of less than 50 \(\upmu\)m and a minimum gap between LM features of approximately 200 \(\upmu\)m. Furthermore, physical scraping of the LM can reduce the gap resolution down to 34 \(\upmu\)m. Finally, to prove the viability of using sprayed LM for elements in wireless communication systems, we demonstrated a sprayed LM patch antenna and transmission line. The measured performance of the LM devices was compared to reference copper versions. The sprayed liquid metal process can be used to make stretchable or conformal electronic circuits.
## 3 Methodology
### Procedures  
To validate the algorithm, the following workflow was evaluated. The experiments started by recording a video lasting at least 4 s in MJPEG format with a frame rate of 30 frames per second, ensuring that at least two samples of each bit were captured. The acquired images were stored for offline processing, composing the videos in a designated directory. The transmitters appearing in the video were programmed following the packet structure depicted in Figure 2. Additionally, these transmitters were positioned  
\begin{table}
\begin{tabular}{l l} \hline \hline Arduino Nano 33 IoT & \\ \hline Microcontroller & Low-power ARM MCU SAMD21 Cortex®-M0+ 32-bit \\ Clock Speed & 48 MHz \\ Flash Memory & 256 Kb \\ Power Supply & 3.3 V, 2.5 A \\ \hline RGB LED & \\ \hline Model & L-154A4 SURKQBDZGW \\ Manufacturer & Kingbright \\ Size & \(5\times 8.6\) mm \\ Wavelength & 470 nm, 525 nm, 630 nm \\ Luminosity & 150–300 mcd, 500–1000 mcd, 600–1300 mcd \\ \hline White LED & \\ \hline Model & SLD430 WBD2 PT3 \\ Manufacturer & ROHM Semiconductor \\ Size & \(5\times 3\times 3\) mm \\ Luminosity & 1850 mcd \\ \hline Raspberry Pi & \\ \hline Model & Raspberry Pi 3 Model B \\ Manufacturer & Raspberry Pi Foundation \\ Operating System & Raspbian-32 bits \\ Processor & Broadcom BCM2837 SoC \\ Storage & 1 GB of RAM \\ Power Supply & 5 V, 2.5 A \\ \hline Camera Module V2 & \\ \hline Model & IMX219—V2 module \\ Manufacturer & Raspberry Pi Foundation \\ Acquisition Mode & Rolling Shutter \\ Maximum Resolution & 3280 \(\times\) 2464 \\ Used Resolution & 1920 \(\times\) 1080 \\ Maximum Gain & 16 B \\ Maximum Frame Rate & 30 FPS \\ FOV & 62.2 \(\times\) 48.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material characteristics.  
Figure 6: Nighttime experimental setup. The resulting link range was between 45 and 75 m.  
at a distance ensuring sub-pixel projection, as determined by the formulas specified in Equations (1) and (2).  
During the offline processing stage, the directory where the video were stored was configured in the main script along with the frames per second rate at which the videos were recorded and the index of the initial image from which detection is desired. This initial image index should be sufficiently small to guarantee the examination of at least 4 additional seconds of images in the video. After this initial setting, the discovery algorithm was executed with these configured parameters, proceeding to the detection and discovery stage and displaying the number of transmitters, their positions and information, among other metrics detailed in the following section. An illustrative diagram of the described process is presented in Figure 7.
## 1 Introduction
Therefore, this study considered the feasibility of a simple machine learning algorithm (i.e., Support Vector Regression (SVR)) and an optimization algorithm (i.e., Particle Swarm Optimization (PSO)), for predicting PM\({}_{10}\) concentration in open-pit mines. A new hybrid model based on an assembly of SVR and PSO, i.e., the PSO-SVR model was developed and proposed in this study for estimating PM\({}_{10}\) concentration from drilling operations. The obtained results by the proposed PSO-SVR model was compared to and evaluated against the other models, including k-nearest neighbors (KNN), random forest (RF), and classification and regression trees (CART). This study is useful for the environmental science community and practical engineering in minimizing the effect of dust concentration on the surrounding environment.
## 2 Methods for Evaluation of Erythrocyte Deformability
### Micropipette Aspiration  
The micropipette aspiration was the first experimental technique used to measure RBC deformability. In this method, a single red blood cell is aspirated into a micropipette with a diameter smaller than the cell itself, applying mechanical stress [5,19,20]. The technique evaluates the cell's ability to deform and move through the narrow pipette, simulating microvascular conditions. Early studies used this method to compare normal vs. stored (or damaged) RBCs, showing that stored RBCs were less deformable than fresh ones [20]. Accurate micropipette aspiration is low throughput and labor-intensive, making it less practical for large-scale analyses. Nonetheless, it laid the groundwork for later single-cell microfluidic methods [5].  
Subsequent variations of these micropipette studies further examined the localized elasticity of the membrane in both intact cells and RBC \"ghosts\", using ever-smaller micropipettes to deform a small segment of the membrane in order to characterize static deformability via membrane extensional rigidity and bending rigidity. To further characterize the dynamic deformability of the cells, the time constants for rapid elastic recovery from extensional and bending deformations were also quantified.  
While micropipette aspiration is valuable for studying single-cell mechanics, it has important limitations:  
* It does not effectively capture the biomechanical heterogeneity of even relatively uniform populations like normal RBCs and is even less-suited for the diverse white blood cell (WBC) population [5].
* The technique is useful for analyzing static and dynamic properties of individual cells and for determining cytomechanical parameters with high precision.
* However, it is low-throughput, only suitable for small sample sizes, and suffers from variability in micropipette fabrication, which reduces consistency.  
Due to these drawbacks, particularly in large-scale or population-wide studies, newer, more scalable techniques were developed, especially those that allow analysis under flow-like, physiologically relevant conditions using microfluidics. These modern methods enable the study of many cells simultaneously, providing a better representation of the overall microrheological properties of blood.  
[PERSON] et al. [18] developed a high-throughput microfluidic device designed to measure the Young's modulus (a measure of stiffness) of individual red blood cells (RBCs). The device features a single microchannel that opens into a funnel-shaped region, with a semicircular obstacle positioned at the entrance of the funnel. As RBCs flow through the device and interact with the obstacle, their deformation under flow conditions is analyzed to determine their mechanical stiffness.  
This method enables the measurement of many single cells, making it possible to quantify heterogeneity in stiffness across the RBC population--something that low-throughput techniques, such as micropipette aspiration, cannot achieve efficiently. It represents a significant advancement in the precision and scalability of RBC mechanical property assessment.
# Tightly-Coupled Data Compression for Efficient Face Alignment
## 5 Results
### Parameter Sensitivity Analysis  
There are two important parameters in our proposed method, one is the number of bits \(nb=\log_{2}Q\) used to encode each element in \(\mathbf{A}_{d}\). The other is the number of regression steps \(D\). Table 3 shows the normalized mean error loss against the standard SDM for different choices of \(nb\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Face Parts** & **Our Proposed Method** & **The Method with Random Initialization (100 Trials)** \\ \hline Face contour & 0.0427 & 0.0568 \(\pm\) 0.0047 \\ Eyebrows & 0.0407 & 0.0559 \(\pm\) 0.0038 \\ Eyes & 0.0287 & 0.0401 \(\pm\) 0.0032 \\ Nose & 0.0272 & 0.0396 \(\pm\) 0.0035 \\ Mouth & 0.0359 & 0.0512 \(\pm\) 0.0052 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of normalized mean errors of our proposed method versus the method with random initialization for the K-means algorithm.  
Figure 13: Normalized mean errors of our proposed method versus the method without K-means clustering.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**\(nb\)** & **The Normalized Mean Error Loss against the Standard SDM** \\  
It can be concluded from the table that the normalized mean error loss against the standard SDM decreased almost linearly when the number of bits was not smaller than 6. The error loss difference between \(nb=6\) and \(nb=16\) was quite small and was hardly noticeable in video applications. The above data justified the choice of \(Q=2^{6}=64\) in all our experiments.  
Experiments for the normalized mean error loss against the standard SDM with different choices of regression steps \(D\) were also conducted in this section. Table 4 shows the results.  
This table verifies that the normalized mean error loss was not sensitive to the choice of the number of regression steps if \(D>1\). This justifies the robustness of our proposed algorithm. In reality, it is uncommon to choose very small \(D\) values unless the computing resources are extremely restricted since feature localization accuracy is not guaranteed even with the standard SDM. This table also reveals the fact that with our proposed method we are free to choose \(D\) according to the demand of the application because the accuracy loss is not sensitive to \(D\).
## 6 Simulation Verification and Analysis  
To demonstrate the feasibility of the TDC-VSG adaptive control strategy, a TDC-VSG converter model was constructed using Matlab/Simulink, and the specific structure of the TDC-VSG is illustrated in Figure 2. Utilizing this model, a comparative analysis of the control performance between TVSG control, traditional adaptive control, and TDC-VSG adaptive control strategies is conducted to highlight the superiority of the proposed  
Figure 11: The improved output characteristics of the TDC-VSG under different control coefficients.  
Figure 10: Feasible region for \(f_{\omega}\) and \(f_{\rm cp}\).  
strategy under conditions of given power and grid frequency variations. The parameters for the simulation are detailed in Table 2, and the specific simulation conditions are as follows: the given power adjustment command is from 5 kW to 15 kW at 2 s, while the grid frequency decreases from 50 Hz to 49.9 Hz at 4 s.
## 4 Future Research Trends for ML-Based IRS-Assisted Wireless Communication
### Dynamic Hybrid Beamforming  
IRS devices can create controllable beamforming by reflecting the BS signal. The beamforming depends on the passive elements on the IRS device and can generate UE-specific formation. ML algorithms can be efficiently applied to estimate effective beamforming for the UE service. In addition, UE feedback can be added to make the dynamic beamforming for mobile UEs. New training data and models need to be investigated for optimal performance.
# Applications of Plasma Produced with Electrical Discharges in Gases for Agriculture and Biomedicine
## 3 Review of Plasma Applications in Agriculture and Medicine
### Plasma Application in Biomedicine
#### 3.2.1 Antimicrobial Applications
In [179], the results of the inactivating effect of the DBD plasma of _Enterococcus faecalis_ (Gram-positive), _P. aeruginosa_ (Gram-negative) bacteria, and _Candida albicans_ yeasts was described. The researchers reported that due to the differences in cell structure, _C. albicans_ demonstrate higher plasma agent resistance than the Gram-positive bacteria. Research [180] conducted on _S. cerevisiae_ cells with the use of dry air plasma generation exhibited a dependence between sterilisation effectiveness and the plasma synthesis parameters (applied voltage, gas composition, exposure time). References [181; 182; 183] inform that LTP can be efficient in inactivation of enzymes associated with plasma-reactive species interaction with the secondary structure of protein. Figure 5: Mechanisms of microbial inactivation with plasma agents [168].
# A Semantic Framework for Decision Making in Forest Fire Emergencies
## 4 Ontology Overview
\begin{table}
\begin{tabular}{c c} \hline \hline
**Code** & **Description** \\ \hline CQ19. & What is the FWI anomaly in a specific area? \\ CQ20. & What is the drought anomaly in a specific area? \\ CQ21. & What is the duff moisture anomaly in a specific area? \\ CQ22. & What is the fine fuel moisture anomaly in a specific area? \\ CQ23. & What are the weather conditions in order to conduct the prescribed burning process in a specific area? \\ CQ24. & What is the location of the alert/recommendation? \\ CQ25. & What is the description of the alert/recommendation? \\ CQ26. & What is the scope of the alert/recommendation? \\ CQ27. & What is the severity level of the alert? \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sample CQs that the data should be able to answer. Figure 2: Main classes in initial ONTO-SAFE ontology. The extended version of the ONTO-SAFE ontology aims to respond to user requirements that are mapped to CQs and capture information regarding weather anomalies, proper weather data for prescribed burning, and the decision support aspect. Thus, the updated version of ONTO-SAFE ontology contains:  
* Weather and forecast anomalies of the Fire Weather Index (FWI), drought, duff moisture, and fine fuel moisture;
* Weather conditions of the prescribed burning process;
* Alerts and recommendations.
## 7 Analysis of Working Fluids for Low-Temperature ORC  
Selecting an appropriate working fluid is a critical aspect in the design of an energy recovery system based on an ORC architecture [88, 89]. The design and evaluation of evaporation and condensation processes are crucial. Matching the critical temperature of the ORC fluid with the maximum temperature of the thermal source is essential, enabling the system to attain the maximum saturation pressure on the fluid. Similarly, managing condensation is essential to optimize the ORC energy performance. A compilation of potential working fluids is provided to assist technical designers in this initial stage. Table 3 serves as a comprehensive database for these fluids, detailing parameters such as the recognition code, ozone depletion potential (ODP), and global warming potential (GWP), along with key thermophysical properties like molecular mass, boiling point, critical temperature and pressure, and latent heat of evaporation, according to the literature [90, 91, 92, 93] and chemical databases. ODP indicates the potential negative impact on the ozone layer, with trichlorofluoromethane (R-11) as a benchmark (i.e., ODP = 1). On the other hand, GWP measures the greenhouse effect compared to carbon dioxide (CO\({}_{2}\)), which has a standard potential of 1. As also anticipated, selecting the appropriate working fluid for a bottoming plant powered by WHR from data centers involves considering various critical factors. These include the fluid's boiling point, critical temperature, environmental impact, and availability. Usually, proper organic fluids present critical temperatures 25-35 \({}^{\circ}\)C lower than the source thermal level when the waste heat target temperature is unconstrained. Organic
## 1 Introduction
However, due to the wide variety of fruits and certain spectral differences between different varieties, the development of a universal model for multi-variety fruit quality testing is important for the improvement of testing efficiency, reducing in equipment costs, and the fulfillment of global market demand. Hyperspectral imaging techniques are capable of acquiring high-dimensional spectral data, but the question of how to efficiently extract valuable information from these complex datasets remains a major challenge at present. In recent years, machine learning methods have shown significant advantages in the field of hyperspectral data analysis by virtue of their powerful data processing and analysis capabilities [20]. PLSR, a classical machine learning method adopted in this study, was able to show excellent performance in multi-variety blueberry SSC prediction. In addition, in the process of constructing a universal model, screening out the key wavelengths that contribute significantly to the model performance is essential to improve the detection efficiency and enhance the model quality. However, due to the significant heterogeneity of spectral features among blueberry varieties, this intrinsic difference not only increases the difficulty of feature wavelengthscreening, but also puts higher demands on the robustness and the ability of prediction models to generalize. At present, the relevant literature on the universal model of multi-variety blueberry quality detection is relatively limited. Compared with the existing research, this paper mainly focuses on the use of a joint preprocessing method and multi-level wavelength screening strategy to realize the fusion of multi-variety data and the generalization training of different scene models, so as to overcome the limitations of cross-variety detection and maintain the efficiency of the model in limited scenarios. The purpose of this study is to achieve the following specific objectives: (1) to construct a universal prediction model applicable to three varieties of blueberry SSC based on hyperspectral imaging; (2) to explore methods for eliminating inter-variety heterogeneity and to assess the feasibility of establishing a multi-species universal model; and (3) to optimize the model structure through multilevel feature selection to achieve improved detection efficacy.
# Research of Calibration Method for Industrial Robot Based on Error Model of Position
## 3 DCP Position Error Model
### Robot Kinematics Parameter Errors  
Because of wear and deformation, the parameters of the robot's links have errors, which lead to the deviation between the actual value and the nominal value of the robot's kinematics model parameters. The parameter errors affecting the robot pose error are  
\[\begin{array}{l}\Delta\theta=[\Delta\theta_{1},\Delta\theta_{2},\Delta\theta _{3},\Delta\theta_{4},\Delta\theta_{5},\Delta\theta_{6}];\\ \Delta a=[\Delta a_{1},\Delta a_{2},\Delta a_{3},\Delta a_{4},\Delta a_{5}, \Delta a_{6}];\\ \Delta\pi=[\Delta a_{1},\Delta a_{2},\Delta a_{3},\Delta a_{4},\Delta a_{5}, \Delta a_{6}];\\ \Delta d=[\Delta d_{1},\Delta d_{2},\Delta d_{3},\Delta d_{4},\Delta a_{5}, \Delta d_{6}];\\ \Delta\beta=[\Delta\beta_{1},\Delta\beta_{2},\Delta\beta_{3},\Delta\beta_{4}, \Delta\beta_{5},\Delta\beta_{6}].\end{array}\]  
where \(\Delta\beta_{i}\) only exists parallel axis, the non-parallel axis directly causes \(\Delta\beta_{i}=0\).  
The actual transformation relationship between the RBCS and FCS can be represented by a model with parameter errors:  
\[\begin{array}{l}\overset{6}{0}T=\overset{1}{0}T\overset{2}{1}T\overset{2}{ 3}T\overset{2}{3}T\overset{4}{3}T\overset{5}{4}\overset{T}{5}T\overset{6}{5}T\\ =\overset{6}{\prod}_{i=1}^{6}R(z_{i-1},\theta_{i}+\Delta\theta_{i})\cdot T(z_ {i-1},d_{i}+\Delta d_{i})\cdot T(x_{i},a_{i}+\Delta a_{i})\cdot R(x_{i},\alpha _{i}+\Delta\alpha_{i})\cdot\\ R(y_{i},\beta_{i}+\Delta\beta_{i}).\end{array} \tag{13}\]  
In the equation, \(R\) means rotation transformation, and \(T\) means translation transformation.
# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 2 Materials and Methods  
This section outlines the detailed methodology for developing, implementing, and validating the RPSC, a robotic system designed to enhance safety and efficiency in road maintenance operations. The RPSC aims to test the hypothesis that an autonomous robotic cone, developed under the European regulatory environment. can achieve positioning accuracy below 30 cm, navigate predefined routes with deviations less than 15 cm, and detect obstacles within a 4 m range, surpassing traditional traffic cones in safety, operational efficiency, and regulatory compliance. The methodology is structured in four phases:requirements identification, design, implementation, and validation through case studies. The approach adheres to European standards and utilises commercial off-the-shelf (COTS) components to optimise costs and reliability.