esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Influencing Factors on Soil Properties Variability
#### 3.3.3 Importance of Influencing Factors in Natural Soils  
To further investigate the importance of influencing factors and the relationship between soil properties and soil heavy metals, we used CCA to quantify the relative importance. The CCA method screened out the importance of those factors. The results of the influencing factor and heavy metal relationship are shown in Figure 3. The CCA figure can be displayed in an ordination diagram in which scores for the influencing factors are indicated as arrows and scores of soil properties represented by points. The CCA showed that the cumulative percentage variance of the first and second axis were 75% and 92%. Monte Carlo permutation tests confirmed the overall significance of the canonical ordination (\(p=0.01\)) and the significance of the first axes (\(p=0.02\)). This result confirmed that both axes jointly represented soil heavy metal distribution, as determined from the CCA ordination. From the figure, we can derive that distance is the most influencing factor in this region. Layer coverage, topography and soil intrinsic properties also impact soil heavy metals. The order is distance > layer coverage > relative elevation > soil properties (pH, TP, and TK).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Land Uses** & **As** & **Cd** & **Cr** & **Cu** & **Pb** & **Zn** & **Ni** \\ \hline Grassland & 6.86 (4.57) & 0.50 (0.30) & 135.52 (67.37) & 28.35 (16.63) & 37.20 (19.13) & 73.47 (27.92) & 56.66 (70.91) \\ Secondary forest & 5.48 (5.12) & 0.37 (0.13) & 66.27 (14.64) & 17.71 (7.57) & 29.06 (12.59) & 55.91 (22.52) & 22.04 (9.03) \\ Shrub land & 6.31 (5.60) & 0.38 (0.12) & 87.95 (41.46) & 21.90 (4.93) & 35.57 (8.68) & 68.48 (11.36) & 30.89 (6.44) \\ Dry land & 2.97 (2.67) & 0.30 (0.07) & 77.77 (21.16) & 21.04 (3.36) & 26.51 (5.95) & 48.26 (12.16) & 24.90 (6.27) \\ Artificial forest & 3.36 (2.46) & 0.60 (0.65) & 82.93 (38.87) & 17.43 (6.54) & 23.55 (7.99) & 41.91 (20.57) & 31.95 (4.04) \\ Paddy field & 5.85 (5.10) & 0.61 (0.32) & 80.34 (50.76) & 20.32 (8.14) & 29.37 (16.16) & 74.81 (14.04) & 36.86 (14.57) \\ Natural forest & 6.15 (8.53) & 0.44 (0.22) & 86.80 (32.35) & 18.82 (11.23) & 25.68 (8.48) & 52.56 (20.56) & 25.96 (16.28) \\ \(F\) value & 1.46 ns & 0.49 ns & 4.99 ** & 2.51 * & 2.76 * & 3.56 ** & 1.82 ns \\ \hline \hline \end{tabular}  
* Numbers in the brackets denote the standard deviation.
* Significant at the 5% level of probability. *
* Significant at the 1% level of probability. ns indicates no significance.  
\end{table}
Table 5: Heavy metals under different land uses.  
Figure 3: Ordination diagram resulting from CCA on soil heavy metals with influencing factors.
## 1 Introduction
Moreover, the fractions of plasma constituents were found to be small and therefore no significant additional heat was produced as a result of reactions in the discharge, so the bulk temperature of the carrier gas would be the dominating factor in determining the plume temperature [27; 28; 29]. From the previous studies, the two main challenges regarding the treatment of blood related diseases such leukemia are: (1) the contact between the plasma and the blood needs to be maximized; and (2) at the same time the blood coagulation needs to be minimized so that maximum contact could be achieved. In order to maximize the efficiency and effectiveness of these treatments and at the same time overcome the mentioned challenges, the contact/mixing between the plasma plume and blood needs to be enhanced dynamically; this requires a versatile multiphysics model for thermofluid analysis of the cold plasmas which has been somehow overlooked in most previous investigations. A parameterized model was set up by [PERSON] et al. [30] to simulate the species densities generated by a plasma needle and also the heat transfer to a skin layer. However, the model developed by [PERSON] et al. did not consider the plasma plume and its transfer to the treatment zone. Finite element analyses were also previously employed in simulation of atmospheric pressure plasma needles, without considering thermofluid analyses [31; 32]. In the present work, two different conceptual designs of the mixing chamber were studied to accomplish a better mixing and to increase the contact between the blood and plasma plume by modifying the previous multiphysics model based on the level set method coupled with heat transfer [29]. It is remarked here that the present designs (i.e., with and without obstacles) of mixing chambers for blood and plasma only serve to demonstrate the concept of enhanced mixing by addition of obstacles to the flow. To the best of our knowledge, there were no previous designs in the literature. Upon an even mixing between the cold plasma and blood with precise probing of thermofluid parameters, an effective mixing could be achieved despite the relatively higher blood viscosity [24] compared to the plasma. Furthermore, information would be provided by dynamic tracking of both fluids in the mixing chambers on the underlying mechanism of mixing between the blood and cold plasma plume.
## 4 Results and Discussion
In this case, the significant variation in Cu concentrations shown by the multivariate test may be the result of the presence of anomalous concentrations of this metal in some soil samples. Conservative post-hoc tests, such as the Scheffe test, are more resistant to the occurrence of anomalous values and, except for Cu, no lack of differentiation of concentrations of the analysed metals depending on the method of their determination was observed. Similar observations can be made from the analysis of the expected values of the marginal averages (Figure 5). Similar results were presented by [PERSON] et al. [54]. These authors noted that even the method of mineralisation can influence the test results, with metals such as As, Cd or Zn. As they state, when dealing with the solution of soil samples for determination of Co, Mn and Cu, total mineralization with both HF/HCl/HNO\({}_{3}\) and H\({}_{2}\)O\({}_{2}\)/HF/HNO\({}_{3}\) mixtures can be successfully applied due to the lack of significant differences in obtained analyses by ICP-MS and AAS techniques, however, in the case of heavily contaminated samples, it is best to use total distribution with H\({}_{2}\)O\({}_{2}\)/HF/HNO\({}_{3}\). Additionally, at extremely high metal contents, the absorption of fluorescent radiation is equally important as well as the primarily determined metal, which may be the cause of the so-called matrix effect consisting of the change of chemical composition of the sample matrix [54]. The precision determined from the duplicate samples for the determination of heavy metals is shown using a probabilistic plot in Figure 6.  
The precision of heavy metal determinations was found to be satisfactory for all metals determined on both AAS and ICP-MS except Cr determined on AAS. For all determinations, more than half of the points lay below the 50% line. For Mn, Cr, Cu and Pb, individual points were just above the 99% line but did not represent more than 10% of all samples. This indicated adequate precision of determinations of metal concentrations in the studied soil samples. Only the determination of Cr concentrations by AAS showed precision slightly lower than expected (Figure 6). Concentrations of other metals were determined with precision equal to 5%. Figure 5: The Least-squares means for analysed metals. Figure 6: Precision of heavy metal determinations.
# Compact Four-Port Circularly Polarized MIMO X-Band DRA
###### Abstract
* The compact structure of a four-port CP MIMO DRA with the utilization of the deformed shape of the DR and DGS together. Implementing a four-port CP MIMO DRA with high isolation in a compact geometry with the utilization of DGS is a novel technique proposed in the reported research work. Obtaining the compact geometry of the four-port CP MIMO DRA is not available in the literature. * A technique of obtaining the high isolation between the ports along with the CP response and maintaining the four ports in a compact geometry of antenna. * High gain is another advantage of the proposed antenna structure that is generally difficult to obtain in the case of C.
* PDRA.
## 2 Materials and Methods
### Unit Cell Designs and Geometrical Parameters  
The four air unit cells examined in this study, namely, the Basic--Periodic unit cell, Semi--Periodic unit cell, Tapered--Diverging unit cell, and Tapered--Converging unit cell, were characterized by non--dimensional geometrical parameters. The morphology of the Basic--Periodic air unit cell design was determined using the same non--dimensional parameters as previously used [28]. These non--dimensional parameters are \(L/C\) and \(d/C\), where \(L\) is the neck length and \(d\) is the diameter of the cylindrical hole. Figure 2 provides a visual representation of these parameters. To make sure that the hole size does not exceed the cube size, the condition \((C-2L)>d\) should hold (see Figure 2). A total number of \(5\)\(L/C\) values were considered, ranging from 0.1 to 0.3 with an increment of 0.05. A total number of 16 \(d/C\) values were considered, ranging from 0.05 to 0.75 with an increment of 0.05. After taking the design rule into account, a total of 55 normalized design combinations of the Basic--Periodic unit cells were considered. Moving forward, we now shift our focus to the newly developed unit cells known as Semi--Periodic unit cells, Tapered--Diverging unit cells, and Tapered--Converging unit cells. These unit cells share a common geometrical characteristic of having non--uniform cross--sectional holes. The non--dimensional geometrical parameters characterizing the morphology of these unit cells were \(L/C\), \(d_{1}/C\), and \(d_{2}/d_{1}\), where \(L\) is the neck length and \(d_{1}\) is the diameter of the bigger cylindrical hole in the Semi--Periodic unit cell and the maximum diameter of the non--uniform cross--sectional hole in the Tapered--Diverging and Tapered--Converging unit cells. \(d_{2}\) is the smaller cylindrical hole in the Semi--Periodic unit cell and the minimum diameter of the non--uniform cross--sectional hole in the Tapered--Diverging and Tapered--Converging unit cells. A total number of \(5\)\(L/C\) values were considered, which ranged from 0.1 to 0.3 with an increment of 0.05, while 15 \(d_{1}/C\) values were considered, which ranged from 0.1 to 0.75, and 3 \(d_{2}/d_{1}\) values were considered, which were 0.5, 0.7, and 0.9. Similar to the design rule implemented for the Basic--Periodic unit cells, a design criterion was applied to these new--class unit cells where only designs that fulfill the requirement of having a sizing rule of \((C-2L)>d_{1}\) were considered. After taking into the design rule, a total of 150 normalized design combinations were considered in each new class of unit cell design. A total of 505 normalized design combinations were considered in the parametric analysis. For better visualization of the considered design combination, Figure 5 depicts the normalized design combinations considered for both pre--existing and newly developed configurations as sample points in their respective spaces.  
Figure 4: Extensive design framework considered in this work.
# AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications
## 7 Evaluation  
This section specifically evaluates our optimization method for the general DAG model. Readers interested in the evaluation results for the LPG and LMG models are referred to [27]. More specifically, we evaluate our optimization method by answering the following questions:  
* Q1: Is our analytical method able to find near-optimal runnable periods?
* Q2: Is it practical to find real optimal solutions by the exhaustive search method?
* Q3: Is our method practically competitive when optimizing large systems?
###### Abstract  
This review provides a comprehensive analysis of the design, materials, fabrication techniques, and applications of flexible wearable antennas, with a primary focus on their roles in Wireless Body Area Networks (WBANs) and healthcare technologies. Wearable antennas are increasingly vital for applications that require seamless integration with the human body while maintaining optimal performance under deformation and environmental stress. Return loss, gain, bandwidth, efficiency, and the SAR are some of the most important parameters that define the performance of an antenna. Their interactions with human tissues are also studied in greater detail. Such studies are essential to ensure that wearable and body-centric communication systems perform optimally, remain safe, and are in compliance with regulatory standards. Advanced materials, including textiles, polymers, and conductive composites, are analyzed for their electromagnetic properties and mechanical resilience. This study also explores innovative fabrication techniques, such as inkjet printing, screen printing, and embroidery, which enable scalable and cost-effective production. Additionally, solutions for SAR optimization, including the use of metamaterials, electromagnetic band gap (EBG) structures, and frequency-selective surfaces (FSSs), are discussed. This review highlights the transformative potential of wearable antennas in healthcare, the IoT, and next-generation communication systems, emphasizing their adaptability for real-time monitoring and advanced wireless technologies, such as 5G and 6G. The integration of energy harvesting, biocompatible materials, and sustainable manufacturing processes is identified as a future direction, paving the way for wearable antennas to become integral to the evolution of smart healthcare and connected systems.  
flexible wearable antennas; Wireless Body Area Networks (WBANs); specific absorption rate (SAR); metamaterials; antenna fabrication; healthcare monitoring 2025
# Review on System Identification and Mathematical Modeling of Flapping Wing Micro-Aerial Vehicles
## 3 System Identification and Mathematical Modeling of Flapping Wing Micro Air Vehicles (FWMAVs)
### Tailed Flapping Wing Micro-Air Vehicles
#### 3.1.3 System Identification of Kinkade Slow Hawk Ornithopter
\[C_{x} =C_{x0}+C_{x_{i\omega}}\delta_{w}+C_{x_{d}}q+C_{x_{i\omega}}\dot{ \delta}_{w}+C_{x_{d\omega}^{2}}\delta_{w}^{2}\] \[C_{z} =C_{z0}+C_{z_{d}}q+C_{z_{d\omega}}\dot{\delta}_{w}\] \[C_{m} =C_{m0}+C_{m\dot{\omega}_{w}}\delta_{w}\]  
To handle state variables that were periodic with harmonics of the flapping frequency, a collinearity analysis was performed on the model regressors and followed by an eigensystem analysis. The validation was performed by comparing the aerodynamic forces and moments in the second set of flight data with the predicted ones obtained using the identified models. The heave forces and pitching moment were well predicted, however, the longitudinal force model requires improvement by using longer data records with more airspeed excitation. The discrepancies may be due to the fact that the captured flight data were in fact, closed loop data, which would include the controller and actuator dynamics, and the coupling between the wing and tail aerodynamics that were not well-captured by the model structure.
## 7 Simulation Results and Discussion
### Performance Metrics  
In this section, basic performance parameters are defined as:  
* PDR: The ratio of packets successfully received at surface sonobuoys over amount of packets transmitted from each network node during the network operational time. The mathematical expression is given as follows: \[PDR=\frac{P_{sonobuoys}}{P_{totalgon}},\] (37) where, \(P_{sonobuoys}\) shows the quantity of packets delivered at the destination, while \(P_{totalgon}\) depicts the summation of packets generated from each network node.
* Fraction of void nodes: It is the amount of network nodes fail to deliver the data packet over the acoustic communication channel because of unavailability of further forwarder nodes in there transmission range.
* Energy consumption: It is defined as, the energy utilized in transmitting a data packet by a node within its transmission range. It is measured in joules (J).
* End-to-end delay: Time required for transmitting and propagating data from source to destination is called end-to-end delay and its unit is seconds (s).
* Depth adjustment: Net distance covered by a void node to find a forwarder node for resuming network operations is called depth adjustment and it is measured in meters (m).
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 4 Machine Learning and AI for Network Slicing Beyond 5G Systems
### Reinforcement Learning  
In reinforcement learning (RL) algorithm, every node (sensor) continuously learn and captures information to take best decisions by interacting with the environment in order to maximize the advantage by using its own experience. Markov decision and Q-learning are widely used RL algorithms. In Q-learning, a sensor node continuously updates its reward to take the best decision by interacting with the environment. In [32], authors used RL in order to achieve the handover efficiency intelligently.
## 2 Design of Compact and Multi-Stack Electromagnetic Bandgap (CMS-EBG) Structure
### Geometrical Design
The CMS-EBG structure in Figure 2 contains sixteen EBG cells, which is equivalent to a 4 \(\times\) 4 array of the previous planar EBG structure. Remarkably, the CMS-EBG structure occupies only a 2 \(\times\) 2 array horizontally, which is a quarter of the size of the previous EBG array. Furthermore, the number of EBG cells is not limited to the number of EBG cells, which is the case for the \(\times\) 4 array. Figure 2: (**a**) Illustration of the compact and multi-stack electromagnetic bandgap (CMS-EBG) structure with vertical-cell array (VCA) branches (the ground via are not shown for simplicity) and (**b**) highlighted view of the patches and VCA branches of the power layers. herein. It can be extended for a larger number of EBG cells with more layers. In many applications of MPCBs, such as cloud server systems and massive computational devices, the planar area occupied by an EBG structure is more significant than the layer number in an EBG structure. Hence, a multi-stack technique in the CMS-EBG structure is valuable for MPCBs. Moreover, the multi-stack technique has another advantage. All VCA branches are consistently formed by the same narrow patches and via structures using the multi-stack technique. The consistent use of the VCA branch ensures that the electromagnetic characteristics of the CMS-EBG structure can be simply modeled and analyzed using the theory of a periodic structure.
## 2 Materials and Methods
### Control Hardware Structure and Commissioning Process  
The generalised controller model was used to implement MPC on industrial-level control hardware, which is a programmable logic controller (PLC) and an edge computer (EC). Both embedded components are mounted on DIN-rails and are suitable for switch-board cabinets. The PLC is responsible for the system sampling (measurements of the devices, e.g., PV power generation) and for the application of optimal setpoints to underlying control circuits (e.g., battery charge controller). Moreover, the specifications of the concrete controller model (the individual device parameters and the number of devices, i.e., the cardinality of the sets) are defined by a commissioning staff in the PLC in IEC 61131 languages. These specifications are communicated via field bus to the EC, where the modular model composition and the embedded optimisation take place. The hardware structure is depicted schematically in Figure 2. The device separation is conducted for reasons of commissioning, reliability, maintainability, and IT-security as explained in [13]. After the system is specified in the PLC, the optimisation, which is performed every 15 min, works as follows:  
1. A measurement of all configured devices is conducted by the PLC and its connected input and output modules.
2. The system configuration, the component parameters, and the component measurements are communicated to the EC via field bus (PLC to EC).
3. The dynamic system model, depending on the transmitted configuration, is automatically composed in the EC.
4. An economic optimisation is conducted based on PV and power demand predictions and the recently sampled system state (EC).
5. The optimal device setpoints are communicated via field bus (PLC to EC).
6. The device setpoints are applied in the energy system (simulation) by the PLC.  
Figure 2: Block diagram of the control system structure: measurements of the energy system (or a digital twin energy system simulation) are taken by the PLC (Wago PFC200 [19]) and are forwarded to the edge computer (Wago Edge Computer [20]) together with configuration data from commissioning. In the edge computer, the system model is composed, and optimal setpoints are calculated, which are then applied to the energy system by the PLC.
# An Online Energy Management Control for Hybrid Electric Vehicles Based on Neuro-Dynamic Programming
## 2 Preparation of Knowledge about the NDP EMS
### Optimal Control Problem  
In this section, we demonstrate key concepts for the EMS control problem. For example, state vector, control vector, control constraints, and instant cost function.  
In this research, we model the parallel HEV as a discrete-time dynamic system as follows:  
\[x(k+1)=f(x(k),u(k)),k=0,1,\cdots,N-1, \tag{1}\]  
where \(k\) represents the current discrete time step; \(N\) is the total sampling time of the whole driving cycle; \(x(k)\) is the state variables which summarize past information that is relevant for future optimization; \(u(k)\) is the control variables, which is the decision to be selected at time step \(k\) from a given set; \(f(.)\) is a function according to which the HEV updates. We choose torque demand at the driven wheels from the driver \(T_{dem}(k)\), wheel speed \(\omega_{w}\), and battery state of charge \(SOC(k)\) as state variables (e.g., \(x(k)=\left(T_{dem}(k),\omega_{w}(k),SOC(k)\right)^{T}\)) and engine torque \(T_{e}(k)\) and gear ratio \(gr(k)\) as control variables (e.g., \(u(k)=\left(T_{e}(k),gr(k)\right)^{T}\)).  
The optimal control problem for this EMS is to find an optimal control sequence to minimize the fuel economy, and constrain the battery SOC within a reasonable scope during a driving cycle. The optimal control action \(u^{*}(k)\) can be obtained by solving the following Bellman equation:  
\[\left\{\begin{array}{ll}J^{*}(x(k))=\min\limits_{u(k)}[L(x(k),u(k))+J^{*}(x( k+1))],&0\leq k<N-1\\ J^{*}(x(N-1))=\min\limits_{u(N-1)}[L(x(N-1),u(N-1))],&k=N-1\end{array}\right., \tag{2}\]  
where \(J^{*}(k)\) is the optimal cumulated cost function from time step \(k\) to the end of the driving cycle. As for the infinite horizon, Equation (2) will change to the following equation:  
\[\begin{array}{ll}J^{*}(x(k))&=\min\limits_{u(k)}\biggl{\{}\sum\limits_{i=k} ^{\infty}\delta^{i-k}L(x(i),u(i))\biggr{\}}\\ &=\min\limits_{u(k)}E\left\{L(x(k),u(k))+\delta J^{*}(x(k+1))\right\}\end{array}, \tag{3}\]  
where \(\delta\in(0,1)\) is a discount factor, which ensures the total cost function convergence and indicates the importance of future cost to current cost; \(L(x(k),u(k),w(k))\) denotes the cost incurred at time step \(k\).  
\[L(x(k),u(k))=L_{fuel}(k)+\beta L_{SOC}(k), \tag{4}\]  
where \(L_{fuel}(k)\), and \(L_{SOC}(k)\) are the fuel consumption and SOC derivation from a desired value from time step \(k\) to time step \(k+1\), respectively; the weight \(\beta\) provides tunable parameters that meet the SOC constraints.  
When solving the above optimal control problem, the following boundary conditions should be satisfied to guarantee that the parallel HEV components will work properly:  
\[\left\{\begin{array}{ll}\omega_{e\_\min}\leq\omega_{e}(k)\leq\omega_{e\_ \max}\\ T_{e\_\min}(\omega_{e}(k))\leq T_{e}(k)\leq T_{e\_\max}(\omega_{e}(k))\\ T_{m\_\min}(\omega_{m}(k),SOC(k))\leq T_{m}(k)\leq T_{m\_\max}(\omega_{m}(k), SOC(k))\\ SOC_{\min}\leq SOC(k)\leq SOC_{\max}\end{array}\right.. \tag{5}\]