esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Modeling  
The geometry of the three-jet nozzle and the coaxial nozzle were modeled in COMSOL Multiphysics 6.0 as displayed in Figure 5.  
The basic model for the flow simulation is the model of [PERSON]. Due to the symmetry of the problem, one-sixth of each nozzle is simulated. The fluid flow can be described by the continuity equation [9] and the stationary Navier-Stokes equation for incompressible fluids (Ma < 0.3 and therefore pressure changes associated with the dynamic pressure can be neglected) [9].  
\[\rho\
abla\cdot u=0 \tag{1}\]  
\[\rho(u\cdot\
abla)u=\
abla\cdot\left\{-p\cdot I+(\mu+\mu_{t})[\
abla\otimes u +(\
abla\otimes u)^{T}]\right\}+F \tag{2}\]  
where \(\rho\) is the density, \(u\) is the velocity vector, \(p\) is the pressure, \(I\) is the unit tensor, \(\mu\) is the dynamic viscosity, \(\mu_{t}\) is the eddy viscosity according to [PERSON] [10] and \(F\) is the volume force. The Nabla operator is coupled to the velocity vector via the tensor product (symbol:  
Figure 4: Illustration of the symmetry of the three-jet nozzle along the \(y\)-axis. For the three-jet nozzle, the 1 st and 4 th quadrants were measured. For the coaxial nozzle, the measurement of the 1 st quadrant is sufficient.  
Figure 5: Model domains for the CFD simulation. (**Left**): Three-jet nozzle. (**Right**): Coaxial nozzle.  
\(\otimes\)). The values for the eddy viscosity and the wall functions as well as their calculation are given by [PERSON] and [PERSON] [8].  
The oxygen concentration is influenced by the mixing of the various gases involved. The mixing of the gases occurs through diffusion and thermal effects. The model domain contains Ar and air. Air has an oxygen concentration of about 20.9 vol.%. In the following, the different gases are referred to as species.  
The mixing due to diffusion can be described by the general mass transport equation [11].  
\[\frac{\partial}{\partial t}(\rho\omega_{i})+\
abla\cdot(\rho\omega_{i}u)=- \
abla\cdot j_{i}+R_{i} \tag{3}\]  
Since this is a stationary problem, the first, the time-dependent term is omitted. Furthermore, there are no chemical reactions of the species involved. Therefore, the term \(R_{i}\) is obsolete. This term is the rate expression describing the production or consumption of species \(i\). This simplifies Equation (3) to:  
\[\
abla\cdot(\rho\omega_{i}u)=-\
abla\cdot j_{i} \tag{4}\]  
Equation (4) considers with the mass flow of species \(i\)[11] diffusive transport, convective transport and the turbulent mixing of the mass fraction \(\omega_{i}\) of species \(i\). The calculation of the turbulent mixing coefficient \(D_{t}\), the mean molar mass \(M\) as well as the calculation of the mixture-averaged diffusion coefficient \(D_{i}^{m}\) of species \(i\) can be found in Wirth and Wegener [8]. The mixed gas is treated as an ideal gas.  
\[j_{i}=-\rho(D_{i}^{m}+D_{t})(\
abla\omega_{i}+\omega_{i}\
abla M/M) \tag{5}\]  
The basic model of Wirth and Wegener considers the mixing due to diffusion, which is considered in Equations (4) and (5).
## 5 Experimental Verification
### Experimental Analysis
#### 5.3.2. Mul-TAB Result Analysis  
This section analyzes the accuracy, robustness, hyperparameters, TAB3, and multi-head attention mechanism of Mul-TAB.  
1. Accuracy Analysis  
In this study, we proposed a speech emotion recognition model based on the improved TIM-Net network and multimodal fusion and conducted experiments on the IEMOCAP and MELD datasets. The test accuracy of our model is shown in the comparative line charts in Figures 11 and 12. On the IEMOCAP dataset, our model achieved a testing accuracy, as shown in Figure 11, reflected in the best WAR reaching 83.9% and the best UAR reaching 82.0%. For MELD, the best testing accuracy was 63.6%, and the training loss was 0.359, as illustrated in Figure 12. The corresponding confusion matrices for the experiments are shown in Figure 13a on the IEMOCAP dataset, our model performed best in recognizing the \"anger\" emotion category, with an accuracy of 90.5%, while the recognition accuracy of the \"neutral\" emotion category was the lowest at 73.5%. Figure 13b for MELD, our model performed best in recognizing the \"neutral\" emotion category, with an accuracy of 89.9%, while the recognition accuracy of the \"disgust\" emotion category was the lowest at 8.82%.  
of capturing the contextual relationships in the features and obtaining temporal-aware representations, contributes to the improvement of the model's performance.
# A Compact Quad-Element UWB-MIMO Antenna System with Parasitic Decoupling Mechanism
## 4 Results and Discussions
### MIMO Performance Parameters  
The diversity performance of MIMO antennas is evaluated through certain parameters like channel capacity loss (CCL), envelope correlation coefficient (ECC), diversity gain (DG) and total active reflection coefficient (TARC). For an acceptable MIMO performance CCL and ECC should be less than 0.5 whereas TARC should be below 0 dB [30]. ECC, DG and TARC are calculated using the relations given in [3]. ECC values can be approximated through scattering parameters instead of far  
Figure 7: Mutual coupling reduction among antenna elements: (**a**) side-by-side antennas; (**b**) across antennas; (**c**) diagonally-across antennas.  
field patterns. It gives an accurate estimate of the envelope correlation without 3D measurement of the radiation pattern. The relation is assumed for rich isotropic scattering environment and is given in Equation (1):  
\[\frac{\left|S_{11}^{\star}S_{12}+S_{21}^{\star}S_{22}\right|^{2}}{(1-(\left|S_{ 11}\right|_{2}-\left|S_{21}\right|^{2}))(1-(\left|S_{22}\right|^{2}-\left|S_{1 2}\right|^{2}))} \tag{1}\]  
For MIMO systems, multiple antennas affect each other when actively involved in transmission or reception therefore they change the overall operating bandwidth and efficiency. TARC is a new metric that has been used to incorporate the overall combined effect of all antennas in a MIMO system and is calculated by using the Equation (2):  
\[\sqrt{\frac{\left|S_{11}+S_{12}+S_{13}+S_{14}\right|^{2}+\left|S_{21}+S_{22}+S_ {23}+S_{24}\right|^{2}+}{\left|S_{31}+S_{32}+S_{33}+S_{34}\right|^{2}+\left|S_{ 41}+S_{42}+S_{43}+S_{44}\right|^{2}}{4}} \tag{2}\]  
Diversity gain is related to the calculated ECC value of the MIMO antenna system. The relation between ECC and DG is given by the Equation (3)  
\[DG=10\sqrt{1-\left|ECC\right|^{2}} \tag{3}\]  
These parameters for the proposed MIMO antenna are shown in Figure 8. All the parameters have values within the suitable range for all the possible placement of four antennas. CCL is less than 0.3 whereas ECC is less than 0.2, thus ensuring the diversity performance of proposed MIMO antenna. TARC gives the overall match of the MIMO system with the value \(-8\) dB. Other parameters like radiation efficiency and peak gain also highlight performance of the proposed antenna system. The radiation efficiency and peak gain of the proposed MIMO antenna is shown in Figures 8e and 9, respectively. The antenna has reasonable gain values in the entire frequency band with the highest value of 4 dB. The proposed antenna has radiation efficiency above 90% in the complete frequency band thus making it an efficient radiation system.
# Evaluation of Ultrasound Accuracy in Acute Appendicitisitis Diagnosis
## 1 Introduction  
Acute appendicitisis it a quite common abdominal disorder with a lifetime incidence of 7% [1]. Based on clinical and laboratory data, acute appendicitisis considered as a difficult diagnosis and can mimic various medical conditions of urologic, gastrointestinal, or gynecologic origin [2, 3]. In pregnant patients, misdiagnosis and unneeded laparotomy may lead to sever complications and have detrimental consequences on fetal outcome [4]. The degree of inflammation can lead to inconsistent clinical presentation. It has been reported that the perforation risk in acute appendicitisis can increase by 5% every 12 h, meaning that an appendectomy should be performed no later than 36 h after a diagnosis of acute appendicitisis is confirmed [5]. Therefore, any delay in diagnosis can lead to adverseeffects such as perforation and wound abscesses [6]. On the other hand, mortality rate caused by acute appendicitis is reported to be about 0.25% taking into account all age groups [7; 8; 9]. A diagnostic approach that is only based on the patient's history and a clinical examination will lead to negative appendectomy rates of between 16% and 47%. However, that figure drops to between 6% and 10% when medical imaging modalities are used in the diagnostic process [10]. Therefore, an accurate diagnosis is essential to prevent any complication from delayed surgical intervention.  
The diagnostic accuracy for a wide variety of diseases has been improved significantly by imaging modalities. Computed tomography (CT) is the most specific and sensitive for diagnosing appendicitisis [11]. However, CT uses significant amounts of ionizing radiation and has some limitations when imaging children and pregnant women [12]. A non-ionizing imaging technique such as ultrasound (US) can be used instead. In children, using US showed a substantial drop in negative appendectomy rates [13]. The importance of this is in reducing patient exposure to ionizing radiation, particularly in children. US has become increasingly popular as a diagnostic modality since it first became available for diagnosing appendicitis during the 1980s [14; 15]. There are several benefits associated with using US as a diagnostic technique, including safety (no ionizing radiation), low cost, and real-time imaging [16]. However, graded compression ultrasonography has shown different levels of accuracy for diagnosing patients suspected of acute appendicitis. The sensitivity and specificity results range from 44% to 100% and 47% to 99%, respectively. The experience and skill level of the sonographer may account for this variability [17].  
The study aimed to evaluate the diagnostic accuracy of US in acute appendicitis cases in a central hospital in Saudi Arabia compared with histopathology, which is the current gold standard and to determine whether there is an association between the accuracy of US in acute appendicitis and the sonographers' expertise.
## 3 Model Development and Validation
### Element, Boundary Conditions, Loading, and Interactions  
Eight-node solid elements with reduced integration (C3D8R) were used to simulate the concrete core, steel sheets, the loading beam, and the foundation beam. Four-node reduced integration shell elements (S4R) were employed to model steel plates and sleeves. In addition, two-node linear three-dimensional truss elements (T3D2) were utilized to simulate the binding bars. The rubber layer and rubber roll were modeled using eight-node solid hybrid elements with reduced integration (C3D8 RH) due to the incompressibility of the material. A series of mesh size sensitivity studies were carried out to make a balance between the accuracy of the FE simulations and computing efficiency. Based on the sensitivity analysis, a mesh size of 12.5 mm was selected for the elements. The coincident nodes among the elements of different components such as the steel plates, the rubber layer, and the concrete reduced the numerical cost and difficulty to obtain the converged results.  
Considering the symmetric geometry and loading pattern of sandwich walls, only a quarter of the specimen FE model was built and restrained symmetrically. The displacement loading was applied to the loading beam and the bottom surface of the foundation beam was restrained against the motion in all directions.  
Tie algorithm was used in the FE model to simulate the constraint among different components in sandwich walls that included the steel plate-rubber layer adhesion, the steel plate-binding bar bolted connections, and so on. A surface-to-surface contact algorithm was applied to the contact interfaces between the concrete core and the rubber layers. This algorithm adopts a \"hard contact\" algorithm and the \"Penalty Friction contact\" to simulate the behavior in the normal and tangential directions of two interacting surfaces, respectively. A friction coefficient of 0.6 was defined by a sensitivity analysis, which showed that the friction coefficient leads to a limited difference between ultimate compressive resistance and ductility with values less than 5%.
# New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms
## 3 Methods
### Background on Genetic Algorithms
The fitness function is used to weigh the random selection of several individuals of the current population, which will be modified by variability operations (crossover and mutation) or selection (natural selection and elitism) to form a new population, which will become the current population of the next iteration of the algorithm. When some stopping criterion is met, the best individual found is returned.
# Accuracy Analysis of the Measurement of Centre of Gravity and Moment of Inertia with a Swing
###### Abstract  
Floating devices under wave and current loads are typically designed based on numerical methods followed by a validation with experimental investigations. This allows an independent check due to the comparison of two different modelling approaches based on different assumptions. At an early stage of the project, numerical simulations are based on theoretical (ideal) values of the centre of gravity (CG) and moment of inertia (MI). The building process of a scaled model results very often in a requested simplification of certain parts, which can influence the CG and also the MI of the scaled model. Knowing those discrepancies allows us to improve the comparability of both approaches but the measurement of those values is connected with either a higher uncertainty or a high level of effort. A significant improvement of such measurements can be reached by the deployment of a specific experimental set-up. This paper presents the classification of the newly designed swing with a high accuracy inertial inclinometer, which was verified by the marker-based motion capturing system. The achieved experiences are useful for the future use of the set-up as well as similar investigations. The comparison with the theoretical values for the swing as well as an example model showed very good agreements and a high accuracy of few millimetres for the CG and an error smaller 1% for MI.  
centre of gravity; centre of mass; moment of inertia; accuracy analysis; experimental investigation; wave tank testing; validation +
Footnote †: journal: applied sciences
# Innovative Strategies for Combining Solar and Wind Energy with Green Hydrogen Systems
## 2 Renewable Energy Integration
### Current State of Solar and Wind Energy Technologies
#### 2.2.3 Wind Energy Technologies  
As the demand of clean energy continues to grow, wind, a form of solar energy that is caused by the differential heating of the surface of the Earth by the sun, has a known capability of supplying several MW of energy. According to a report published by Statista in 2024, the global generation of wind energy for the past two decades has shown a percentage increment of 379% with an exponential growth rate of 0.12% (see Figure 2). Traditional wind energy generation has primarily depended on onshore wind turbines, which have become a common feature in areas with strong and steady winds. These turbines harness the kinetic energy of the wind, converting it into electrical energy as their blades rotate [28]. According to Greenpeace, wind power has the potential to meet global energy demands, providing up to 30% of the world's electricity by 2050, while also creating over 2 million new jobs and reducing CO\({}_{2}\) emissions by 113 billion tons by that time.  
With the growing demand for sustainable energy, wind generation technology has advanced significantly, leading to cutting-edge developments that have greatly expanded the potential of wind power [29]. One of the most notable advancements is the shift from onshore to offshore wind turbines, which marks a significant leap forward. Offshore turbines can harness stronger and more consistent winds over oceans and large bodies of water, allowing them to generate more electricity than their onshore counterparts due  
Figure 2: Wind energy global generation according to Statista.  
to the higher wind speeds found offshore [30]. Additionally, floating wind turbines, a ground-breaking innovation, allow for the installation of turbines in deep waters where fixed foundations are impractical, opening up vast new areas for wind energy generation [31]. Another emerging technology is the Vertical Axis Wind Turbine (VAWT), which features a design that rotates around a vertical axis. This allows VAWTs to capture wind from any direction, making them ideal for urban settings or areas with turbulent wind conditions [32; 33]. These innovations and many more are propelling wind energy towards a more efficient and sustainable future (see Figure 3).
# Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China
## 1 Introduction
Additionally, SMAP monitoring results and optical remote sensing images can be used for comparison and verification of data obtained from the former. The methodology for processing data is the same as those used described by the authors of [21; 25].
## 3 MPC Controller Design and Longitudinal Speed Compensation
### MPC Formation  
The control objective of the path tracking MPC controller is to solve a convex optimization problem of at each time step. Then the first optimal value of control sequence is used as the future input, and the lateral force sequence is as follows.  
\[F_{\mathrm{y}\mathrm{f}}(k)=[F_{\mathrm{y}\mathrm{f}\mathrm{f}\mathrm{,1}},F_{ \mathrm{y}\mathrm{f}\mathrm{,2}},\cdots,F_{\mathrm{y}\mathrm{f}\mathrm{,}k}, \cdots F_{\mathrm{y}\mathrm{f}\mathrm{,}N_{\mathrm{c}}-1}] \tag{11}\]  
The objective function can be expressed as:  
\[\min_{\mathbf{U}(k)}J_{\mathrm{N}p}=\sum_{i=k}^{k+Np-1}\mathbf{y}^{T}\mathbf{Q }\mathbf{y}+\sum_{i=k}^{k+Nc-1}\Delta u^{T}\mathbf{R}\Delta u+\mathbf{W}\varepsilon \tag{12}\]  
\[s.t.\Big{[}\mathbf{H}_{\mathrm{v}}\mathbf{x}^{i}\Big{]}\leq\mathbf{G}_{ \mathrm{v}},i=0,\cdots,k,\cdots Nc-1 \tag{13}\]  
\[\left|\Delta u(k+i)\right|\leq\Delta u_{\mathrm{max}},i=0,\cdots,k,\cdots,Nc-1 \tag{14}\]  
\[\Delta u(k+i),i=Nc,Nc+1,\cdots,Np-1 \tag{15}\]  
\[\left|u(k+i)\right|\leq u_{\mathrm{max}},i=0,\cdots,k,\cdots,Nc-1 \tag{16}\]where \(\mathbf{U}(k)=[u(k),\varepsilon]\), and \(\varepsilon\) is a non-negative slack variable used to ensure the optimization problem is always feasible. \(N_{\text{P}}\) and \(N_{\text{c}}\) are the prediction horizon and control horizon, respectively. \(\mathbf{Q}\), \(\mathbf{R}\), and \(\mathbf{W}\) are weighting matrices.
## 1 Introduction
However, in contrast to the AMMI model, the GGE method integrates various components of two-dimensional representation such as the 'which-won-where' patterns,'mean versus stability', and 'discrimination and representativeness' of environments, along with the rankings of genotypes and environments, employing singular value decomposition (SVD) [17; 18]. The GGE biplot model is an effective tool for exploring agronomic data, particularly useful for visualizing G \(\times\) E interactions. It not only identifies individuals that exceed average performance and maintain stability across different environments but also highlights those that perform best under specific environmental conditions [19]. Additionally, its application extends beyond responding to variations in locations or planting seasons; it is equally effective in various studies such as water stress response [6], pathogen resistance [20], and other contexts where yield is not the primary response indicator, such as in gene activation [21] and the study of biocontrol microorganisms [22]. In this study, we employed the GGE biplot technique to analyze the G \(\times\) E interaction patterns in nine standard (released) sugarcane varieties and a group of varieties under study called CLONES, with the goal of revealing the most stable and high-performing individuals across different production periods and cultivation zones, as well as analyzing genotype-environment interactions that have the greatest impact on sugarcane yield. The results of this study could provide useful information for the improvement of sugarcane cultivation and are expected to be beneficial to researchers utilizing biplot exploration techniques.
# A Kernel Least Mean Square Algorithm Based on Randomized Feature Networks
## 3 The Mean Square Convergence Analysis  
The convergence performance is the key aspect of an adaptive filter. The energy relation [26] is a unified tool for analyzing the steady state and transient performance of the nonlinear adaptive filter. The energy conservation relation has been used to analyze the convergence performance of the QKLMS algorithm, which is based on the implicit mapping method [14; 27]. In this section, we perform the mean square convergence analysis for the KLMS-RFN by studying the energy flow through each iteration. Firstly, the fundamental energy relation is introduced. Secondly, we present a sufficient condition for mean square convergence. Thirdly, lower and upper bounds on the theoretical value of the steady-state excess mean square error are established.
## 2 Cluster Head Selection
### Assisted Schemes
#### 2.2.2 CHs Assisted Schemes  
CHs can collect the up-to-date states of their cluster members through continual communications in data transmission phases. Using this information, CHs can assist in the selection of the next round CHs to balance the clusters and to eliminate extra energy expenditures in re-clustering stages.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 3 Unmanned Aerial Vehicles (UAVs)
### UAV Standardizations
#### 3.4.2 UAVs Standardization Outside the 3 GPP  
Besides 3 GPP standardization, there are several other regulatory bodies that define various specifications of UAVs to ensure efficient, reliable, and uniform communication.  
* In 2015, the Institute of Electrical and Electronics Engineers (IEEE) introduced the Drones Working Group (DWG). The main aim of this group was to develop the taxonomy for consumer drones with the objective of highlighting security and privacy issues. For this purpose, the DWG establishes methods, systems, requirements, testing, and validation needed for consumer drones to preserve the security and privacy of the public and their properties.
* The European Telecommunications Standards Institute (ETSI) aims to identify UAV applications, use cases, and understanding regarding Internet Protocol (IP) suite  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Link** & **Data Type** & **Data Rate** & **Critical** \\ \hline \multirow{4}{*}{Downlink} & Radio control (PDCCH) & \multirow{2}{*}{N/A} & Yes \\  & Synchronization (SSS/PSS) & & Yes \\  & Command and Control (C\&C) & 60–100 kbps & Yes \\  & Command and Control (C\&C) & 60–100 kbps & Yes \\  & Application data & Up to 50 Mbps & No \\ \hline \hline \end{tabular}
\end{table}
Table 6: UAV communication requirements [24].  
Figure 6: Different examples of UAVs on altitude (**a**) LADP, (**b**) LALE, (**c**) MALE, and (**d**) HALE [56].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**UAV Type** &
\begin{tabular}{c} **Altitude** \\ **(km)** \\ \end{tabular} & **Avg. Control Range (km)** & **Avg. Airspeed (m/s)** \\ \hline Multi-rotor UAVs (DIJ Agras MG-1P [58]) & 2 & 3–5 & 7 \\ Fixed-wing UAVs (AgEagle RX60 [59]) & 0.125 & 2 & 18.8 \\ Single rotor (Alpha 800 [60]) & 3 & 30 & 15.2 \\ Fixed-wing-multi-rotor hybrid UAVs (Jump 20 [61]) & 4 & 500–1000 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Feature-based comparison of UAVs [57].  
architecture to be developed and spectrum rules required to facilitate UAVs in current LTE networks [63].
* The International Telecommunication Union Telecommunication Standardization Sector (ITU-T) defined work item (WI) Y.UAV.arch to support a reliable and functional architecture of UAVs and UAV controllers through IMT-2020 networks [64].