esa-sceva/satcom-chunk-collection huggingface dataset:
# Valkyrie--Design and Development of Gaits for Quadruped Robot Using Particle Swarm Optimization
###### Abstract  
Over the past decades, developments and scientific breakthroughs in the field of robotics have shown the replacement of wheeled robots with legged robots, which are often inspired by the biological characteristics of legged animals. Many industries and urban-based applications promote quadruped robots because of their dexterous ability to efficiently handle multiple tasks in the working environment. Motivated from the recent works in the field of quadruped robots, this research aims to develop and investigate gaits for a 2 DoF mammal-inspired quadruped robot that incorporates 4 hip and 4 knee servo motors as its locomotion element. Forward and inverse kinematic techniques are used to determine the joint angle required for the locomotion and stability calculation are presented to determine the center of mass/center of gravity of the robot. Three types of gaits such as walk, trot, and pace are developed while keeping the center of mass inside the support polygon using a closed-loop control system. To minimize errors and improve the performance of the robot due to its non-linearity, a meta-heuristic algorithm has been developed and addressed in this work. The fitness function is derived based on the Euclidean distance between the target and robot's current position and kinematic equations are used to obtain the relation between joints and coordinates. Based on the literature, particle swarm optimization (PSO) was found to be a promising algorithm for this problem and is developed using Python's 'Pyswarms' package. Experimental studies are carried out quantitatively to determine the convergence characteristics of the control algorithm and to investigate the distance traveled by the robot for different target positions and gaits. Comparison between experimental and theoretical results prove the efficiency of the proposed algorithm and stability of the robot during various gait movements.  
quadruped robot; particle swarm optimization; kinematics; gait development; support polygon +
Footnote †: journal: Applied Sciences
## Materials and Methods  
### Used Materials  
Three of the most commonly applied electro-insulating liquids in electrical power devices were used for the tests. The base liquids used for the research were Nytro Draco mineral oil (MO) produced by Nynas (Stockholm, Sweden), MIDEL 7131 synthetic ester (SE) produced by M&I Materials (Manchester, UK), and Envirotemp FR3 natural ester (NE) produced by Cargil (Minneapolis, MN, USA). The characteristic properties of the electro-insulating liquids used are given in Table 1. Table 2 shows the level of moisture (WCO: Water Content in Oil; and RS: Relative Saturation) and aging parameters (acid number and surface tension) of the investigated liquids.  
The electro-insulating liquids were modified using fullerene C\({}_{60}\) and titanium dioxide TiO\({}_{2}\) nanoparticles. The average particle size of fullerene C\({}_{60}\) (Sigma-Aldrich, CAS 99685-96-8) was 0.7 nm. In turn, the average size of titanium dioxide TiO\({}_{2}\) nanoparticles (Sigma-Aldrich, CAS 13463-67-7,  
\begin{table}
\begin{tabular}{c c c c} \hline
**Property** & **Mineral Oil** & **Synthetic Ester** & **Natural Ester** \\ \hline Density at 20 \({}^{\circ}\)C (kg\(\cdot\)m\({}^{-3}\)) & 887 & 970 & 920 \\ Kinematic viscosity at 40 \({}^{\circ}\)C (mm\({}^{2}\cdot\)s\({}^{-1}\)) & 9 & 28 & 36 \\ Pour point (\({}^{\circ}\)C) & \(-\)54 & \(-\)60 & \(-\)21 \\ Flash point (\({}^{\circ}\)C) & 148 & 275 & 330 \\ Fire point (\({}^{\circ}\)C) & 170 & 322 & 360 \\ Permititivity at 20 \({}^{\circ}\)C & 2.2 & 3.2 & 3.2 \\ Dissipation factor at 90 \({}^{\circ}\)C & 0.08 & \(<\)0.006 & 0.005 \\ Breakdown strength IEC 60,156 2.5 mm (kV) & \(>\)70 & \(>\)75 & 73 \\ \hline \end{tabular}
\end{table}
Table 1: Some physicochemical and dielectric properties of the electro-insulating liquids used [15].  
St. Louis, MO, USA) was 21 nm. To produce stable colloids based on titanium dioxide nanoparticles, it was necessary to use a surfactant (monolaurate sorbitan C\({}_{18}\)H\({}_{34}\)O\({}_{6}\)) with the trade name Span(r) 20 (Sigma-Aldrich, CAS 1338-39-2). The chemical structure of surfactant Span(r) 20 is shown in Figure 1.
## 3 Results and Discussion
### The Effect of Antimicrobials on Biogas Yield
Our experiment proves that anaerobic processing of SS and CS becomes less efficient when exposed to high concentrations of antimicrobial substances [47]. Importantly, both substrates are characterized by the distinctly different composition of their microbiota [48], which means that they differ in the drug sensitivity of particular groups of microorganisms engaged in the AD process. This is manifested by the different extent to which drugs affect the course and efficiency of AD. When SS served as a substrate, MET most strongly affected the efficiency of methane fermentation, causing a decrease in both biogas production and methane content in biogas, as well as the accumulation of most of VFAs. Exposure of CS to MET resulted in a 25% decrease in biogas production, without affecting the biogas content of CH4 or accumulation of VFAs (Table 2). In the case of CS, the broadest spectrum of the process inhibition, similar to that induced by MET in SS, was detected when AMO had been added to the substrate. Exposure of SS to AMO resulted in the inhibition of biogas production and accumulation of only one of VFAs (from ten analysed in total), which was butyric acid. Values of methane production and VFAs concentration in SS and CS from process and control bioreactors were grouped in hierarchical cluster analysis with the use of [PERSON]'s method (Figure 1). Three main clusters were identified. The first cluster (I) included both control bioreactors (SSC, CSC) and SS exposed to OXY (OXY SS). The analysis of the results concerning the production of VFA in individual bioreactors (Table 2) shows that these values for OXY SS were closest to the control bioreactors with SS and CS, which resulted in grouping these three bioreactors in one cluster. MET SS and AMO CS formed the second cluster (II). As a result of exposure of the SS to MET and the CS to AMO, the highest differences compared to the control bioreactors were noted, manifested by the greatest changes in methane production and VFAs accumulation. The distinction of these bioreactors in a separate cluster also reflects the results concerning the parameters of methane fermentation (Table 2). The third cluster (III) consisted of other process bioreactors. The results point to the mentioned differences in the composition and drug-sensitivity of the microbiota that are characteristic of these substrates, with respect to particular antimicrobial substances.
## 5 Simulation Results  
Under the objective of the analysis and comparison of the proposed algorithms, we used MATLAB for the simulation studies and evaluated the proposed algorithms' performances. The parameters utilized in the simulations are given in Table 1. To observe the network lifetimes and remaining network energies, the simulations are conducted up to 3500 rounds. After each 5% SN death, the optimal mobile BS location on the grid is calculated and the mobile BS moved to the calculated position.  
To illustrate our simulations, an instance of 100 scattered SNs followed up by GPS in a 100 m \(\times\) 100 m heterogeneous network field is shown in Figure 6. The mobile BS is located at the center of the sensor deployment area at the beginning of the simulation. For example, if the sensors are deployed in a size \(M\times M\) area, the BS is located at coordinates \(\left(\frac{M}{2},\frac{M}{2}\right)\) at the start of the simulation.  
10% of the nodes are assumed to be superior SNs, which have the highest initial energy levels in the network. 15% of the nodes are assumed to be medium SNs, which have the medium initial energy levels in the network. The remaining 75% of the nodes are assumed to be normal SNs, which have the lowest initial energy levels in the network. As each SN has a chance to be elected as a CH, all SNs can aggregate data that they receive from other SNs including their own collected data.  
In the sensor network field, the separation between adjacent BS virtual points in the grid is set to be 10 m. The BS virtual points on the edges of the sensor network field are omitted. Therefore, in a 100 m \(\times\) 100 m network field, there are 81 BS virtual points. At a particular time during the simulation, the optimum BS location which has the shortest overall distance from all active SNs, can be one of these virtual points.  
Figure 7 illustrates an instance of the sensor network field with the BS virtual points. At this instance, the mobile BS is positioned at coordinates \((60,60)\). For each 5% SN death, the proposed algorithm recalculates the optimal BS location on the grid and the mobile BS moves to that point to collect the data from the remaining alive SNs. Throughout the lifetime of the network, this procedure referred to as the BS mobility is repeated.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameters** & **Values** \\ \hline Size of network & 100 m \(\times\) 100 m \\ \hline Number of sensor nodes (S) & 100 \\ \hline Election probability value of CH \((p)\) & 0.1 \\ \hline Coefficient for amplification signal from free space (\(\in_{\text{fs}}\)) & 10 PJ/bit/m\({}^{2}\) \\ \hline Signal amplification coefficient of multi-path fading (\(\leq_{mp}\)) & 0.0013 PJ/bit/m\({}^{4}\) \\ \hline Energy consumption in electronic circuitry (E\({}_{\text{{\tiny{eflec}}}}\)) & 50 nJ/bit \\ \hline Energy consumption in data aggregation (E\({}_{DA}\)) & 5 nJ/bit \\ \hline Packet size & 4000 bits \\ \hline \end{tabular}
\end{table}
Table 1: The parameters of the simulation.  
Figure 6: An illustration of a 100 m \(\times\) 100 m sensor network field.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 5 Key Cooperative and Cognitive Technologies
### Beamforming  
Beamforming is a signal processing technique used in antenna arrays to have spatial discrimination and filtering abilities [173]. However, beamforming does have limitations, such as high cost and computing resources. Multi-antenna beamforming is an effective means to mitigate co-channel interference and has been widely used in traditional fixed-spectrum-based wireless systems [174].  
Beamforming techniques for hybrid satellite-terrestrial networks have been discussed in [175] to solve the problems of beamforming- and combining-based AF relaying in cooperative HSTNs.  
Aiming to minimize the transmit power without compromising QoS, the authors of [176] investigated relay beamforming schemes for a cooperative HSTN, where a cognitive base station (CBS) with multi-antennas was used not only as a relay to assist with signal transmission between a satellite and a terrestrial primary user (PU), but also as a base station to transmit signals to multiple secondary users (SUs) with known CSIs for both uplink and downlink.  
The outage analysis of a multi-antenna DF relay-based HSTN with beamforming was discussed in [177]. By applying single-antenna relays, the authors of [178] investigatedcooperative beamforming under interference for an HSTN for wjocj a satellite transmits signals to a terrestrial mobile terminal (MT) through the relays.  
Robust multi-objective beamforming was proposed in [179] for an integrated satellite and high-altitude platform network with imperfect CSI by formulating a sum rate maximization and total transmit power minimization trade-off.  
For a spectrum-sharing HSTN in [180], an optimal beamforming scheme was designed by considering the impact of a non-linear power amplifier and imperfect CSI at the transmitter. An overview of the energy-efficient transmission, total power constraint, and per-antenna power constraint beamforming schemes for a UAV-relay-based HSTN were studied in [181].  
A robust downlink hybrid beamforming scheme for a satellite-terrestrial integrated network was considered in [182] under the realistic assumption that the angular information of eavesdroppers was not perfectly known.
## 2 Test Exploration
### Turbulence Model  
Double-row TBRs have complex structures and motion states and involve the two phases of oil and gas. They also operate at high rotational speeds, and the turbulence in the bearing fluid domain is strong. To better simulate the fluid flow on the curved wall, the RNG \(k\)-\(\varepsilon\) turbulence model is adopted in this study [23]. The equations for the turbulence kinetic energy \(k\) and the turbulent dissipation rate \(\varepsilon\) are respectively as follows:  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ku_{i})}{\partial x_{ i}}=\frac{\partial}{\partial x_{j}}[\alpha_{k}\mu_{eff}\frac{\partial k}{ \partial x_{j}}]+G_{k}+\rho\varepsilon \tag{4}\]  
\[\frac{\partial(\rho\varepsilon)}{\partial t}+\frac{\partial(\rho\varepsilon u _{i})}{\partial x_{i}}=\frac{\partial}{\partial x_{j}}[\alpha_{\varepsilon} \mu_{eff}\frac{\partial\varepsilon}{\partial x_{j}}]+\frac{C_{1\varepsilon }\varepsilon}{k}G_{k}-C_{2\varepsilon}\rho\frac{\varepsilon^{2}}{k} \tag{5}\]  
\[\mu_{eff}=\mu+\mu_{t} \tag{6}\]  
where \(\mu_{eff}\) is the total viscosity of the lubricating oil; \(\mu\) is the dynamic viscosity of the lubricating oil; \(\mu_{t}\) is the eddy viscosity caused by the local turbulent motion of the lubricating oil; \(G_{k}\) is the production term of the turbulence kinetic energy \(k\) caused by the average velocity gradient of the lubricating oil; \(\alpha_{k}\) and \(\alpha_{\varepsilon}\) are respectively the reciprocals of the effective Prandtl numbers of the turbulence kinetic energy and turbulent dissipation rate, and \(C_{1\varepsilon}\) and \(C_{2\varepsilon}\) are model constants.
# Physiological Responses Related to Sitting Comfort Due to Changes in Seat Parameters
## 2 Materials and Methods
### Measurement
#### 2.3.2 Physiological States  
Electroencephalograms (EEG) were recorded to calculate indices reflecting central nervous system activity using a 32-channel Brain Vision System (Brain Products GmbH, Hohenbrunn, Germany) with active electrodes based on the international 10/20 system [23]. Specifically, measurements were taken from regions of the frontal lobe (F3, Fz, F4) associated with information integration, the central region (C3, Cz, C4) related to somatosensation, and the parietal lobe (P3, Pz, P4) related to spatial information processing at a sampling frequency of 500 Hz. Artifacts derived from eye movements, muscle activity, and heartbeat [24; 25] were removed using the EEGLAB program 2021.1, a MATLAB toolbox (version: 2021.1, [26]), and the Fast Fourier Transform (FFT) was performed. Subsequently, the power spectral density of theta (\(\theta\), 4-8 Hz), alpha (\(\alpha\), 8-13 Hz), and beta (\(\beta\), 13-30 Hz) waves in the measured channels was computed, and the degree of central nervous inactivity (\(\theta+\alpha\))/\(\beta\)[3; 27; 28] was determined for each region.  
To compute indices reflecting autonomic nervous system activity, electrocardiograms (ECGs) and respiration rates were measured at a sampling rate of 1000 Hz using sensors (Biosignalplux, PLUX wireless biosignals S.A., Lisbon, Portugal). The R wave was detected by a MATLAB program from the ECG waveform, and the time series of the R wave interval (RRI) and the average of RRI was calculated. Furthermore, the power spectral density of the very-low-frequency (VLF, 0.001-0.04 Hz), low-frequency (LF, 0.04-0.15 Hz), and high-frequency (HF, 0.15-0.45 Hz) bands was extracted from the RRI time series using the FFT. For the HF component, the influence of respiratory variability was removed based on respiratory frequency data, the LF/HF value was calculated as an index of sympathetic activity, and the HF content was calculated as an index reflecting parasympathetic activity [29].  
Figure 3: Segmentation of the measurement area.
## 2 Materials and Methods
### Data Analysis
#### 2.5.3 Thermodynamic Study  
Temperature is an indicator of the nature of the adsorption, that is, whether it is an exothermic (\(\mathrm{\Delta H^{o}<0}\)) or endothermic (\(\mathrm{\Delta H^{o}>0}\)) process. Measurement of the adsorption heat \(\mathrm{\Delta H^{o}}\) is the main criterion for differentiating chemisorption from physisorption [32].  
\(\mathrm{\Delta G^{o}}\), \(\mathrm{\Delta H^{o}}\) and \(\mathrm{\Delta S^{o}}\) are the thermodynamic parameters that determine the feasibility and spontaneity of the adsorption process. They were determined using the following relations [7,33]:  
\[\mathrm{K_{ad}}\ =\frac{\mathrm{q_{e}}}{C_{e}} \tag{14}\]  
\[\mathrm{\Delta G}=-\mathrm{RT.\ln K_{ad}} \tag{15}\]  
\[\mathrm{ln\ K_{ad}}\ =\frac{\mathrm{\Delta S}}{\mathrm{R}}-\frac{\mathrm{\Delta H }}{\mathrm{RT}} \tag{16}\]  
where, \(\mathrm{K_{ad}}\) (L/g) is the distribution constant, \(\mathrm{q_{e}}\) (mg/g) represents the equilibrium solid-phase concentration, \(\mathrm{\Delta G^{o}}\) (J/mol) is the standard free energy change, \(\mathrm{\Delta H^{o}}\) (J/mol) is the standard enthalpy change and \(\mathrm{\Delta S^{o}}\) (J/mol.K) is the standard entropy change, \(R\) is the universal gas constant (J/mol. K), and T is the absolute temperature (K).
# The Application of Satellite Image Analysis in Oil Spill Detection
## 2 Materials and Methods
### Methodology Description
#### 2.2.2 Indices and Classification Methods  
For the purposes of this work, the following indices were used:  
* NDVI \[\mathrm{NDVI}=\frac{\mathrm{NIR-RED}}{\mathrm{NIR+RED}}\] (1)  
NDVI is an indicator for determining the state of vegetation [38; 39; 40]. NDVI is based on the contrast between the greatest near infrared reflection and the red absorption. The range of NDVI values is \(-1\) to \(1\). Negative NDVI values (values close to \(-1\)) correspond to water. Values close to zero (\(-0.1\)-\(0.1\)) correspond to barren regions of rock, sand, or snow. Low, positive values are for shrubs and grasslands (values from about \(0.2\) to \(0.4\)), while high values are for moderate and tropical rainforests (values close to \(1\)).
* NDWI2 \[\mathrm{NDWI2}=\frac{\mathrm{GREEN-NIR}}{\mathrm{GREEN+NIR}}\] (2)  
NDWI2 is used to monitor changes in water content in water reservoirs. Since water reservoirs strongly absorb light in the visible and infrared electromagnetic spectrum, green and near infrared bands are used to illuminate water reservoirs. Index values greater than \(0.3\) usually relate to water bodies. The higher the value, the greater the content of healthy water in a given tank. On the other hand, results below \(0.3\) indicate no water [41].
* NDSI \[\mathrm{NDSI}=\frac{\mathrm{RED-SWIR}}{\mathrm{RED+SWIR}}\] (3)  
NDSII relies on the ratio of visible and shortwave infrared wavelengths to separate snow/ice pixels from cloud cover and other undesirable elements. It is calculated by dividing the red and short infrared bands by their sum, because snow indicates the lowest and the highest reflectance in these bands, respectively. The index values range from \(-1\) to \(1\). When surveying areas covered with a significant amount of snow, including Alaska, Minnesota, and Iceland, the NDSI mapped the snow cover well and distinguished it from clouds for a value of about \(0.4\)[42].
* SWM \[\mathrm{SWM}=\frac{\mathrm{BLUE+GREEN}}{\mathrm{NIR+SWIR}}\] (4)  
SWM provides fast and effective water detection. It is dedicated to Sentinel-2 products. It is calculated as the sum of the blue and green bands divided by the sum of the near and short infrared bands. The range of index values is from \(0\)-\(12\), and the optimal threshold is values in the range \(1.4\)-\(1.6\), when the index reaches an accuracy of \(96\)-\(99\)% depending on the test area [43].
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 3 O-RAN in Smart IoT Systems
### Smart Healthcare Systems  
The smart healthcare system entails leveraging technological advancements in AI/ML and IoT to improve healthcare delivery, enhance medical services, improve patient outcomes, and streamline healthcare processes. The fundamental objective of smart healthcare is to provide personalized, efficient, and effective healthcare services while minimizing costs and enhancing the general quality of caregiving. Some of the critical components of smart healthcare include IoT devices (sensors/actuators) and healthcare wearables for collecting patients' data remotely. The data are analyzed using AI/ML models to predict the required action, processed and stored with the aid of computing infrastructures like the network edge, cloud, data centers, and other networking equipment, and supported with a user interface to guide user interaction with the system. Due to the critical nature of the system and the data in circulation, the system is supported with authentication, encryption, access control, and intrusion detection mechanisms to ensure the security and privacy of the system and data [82]. With the benefits realized from the advancements in cellular networks like the 5G/6G, O-RAN can improve the services rendered by IHS by providing a flexible, scalable, and interoperable architecture that can support many devices, users, and systems to operate more cost-effectively and energy-efficiently. For example, [PERSON] et al. [62] presented \"OpenCare5G\", a project aimed at demonstrating 5G-enabled telemedicine, i.e., remote digital healthcare examinations in real-time. The project employs the O-RAN concept to disaggregate the network components, giving room for customization and updates and making the solution flexible and cost-effective. Likewise, [PERSON] et al. [83] demonstrated how O-RAN uses AI/ML to improve network performance and supported that a policy-driven use case could control the inactivity timer prediction of narrow-band medical IoT devices. **Takeaways from smart healthcare systems:** Supported by Table 4, the reviewed efforts in smart healthcare covered \(Y_{1}\) and \(Y_{2}\), while \(Y_{3}\) and \(Y_{4}\) are yet to be explored. However, despite O-RAN's achievements in smart healthcare, there is a need for more efforts to transition the several objectives in smart healthcare to reality as well as healthcare-compatible