esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Elimination of Invalid Configurations in the Solution Space
The initial population is generated randomly and consists of bit arrays representing adaptive-fixed connections. For example, for 3-row PV arrays, each bit array is 18 bits long. The best individuals (chromosomes) are selected based on their CVI values, which are represented by the fitness function (Algorithm 3). Through mutations in each iteration, the transfer of better individuals to the next generation is targeted. The process of minimizing the CVIvalue continues in this manner until the best fitness value, which provides the optimal configuration, is determined. The algorithm takes as input the fixed and adaptive panel arrays, the number of chromosomes, and GA parameters such as crossover and mutation rates. It also initializes variables, including the population size and a threshold value for fitness convergence. The concepts from Algorithm 2 are outlined below:  
_Fitness Evaluation_: In each iteration, the fitness values of all chromosomes in the population are calculated using the fitness_function. This function calculates the CVI value for a given panel configuration. The best fitness value and the corresponding chromosome are recorded. _Selection and New Population Creation_:  
* Selection: Two parent chromosomes are randomly selected from the population. * Crossover: A crossover operation is performed with a probability defined by crossover-Rate. A segment of the chromosome is exchanged between the parents to create a new child chromosome. * Mutation: Mutation is performed on each gene of the child chromosome with a probability defined by mutationRate and the binary value of the gene is converted to its complement. * Validation: The child chromosome is validated against the problem constraints using the valid_configuration function (Algorithm 1). If valid, it is added to the new population. _Population Update:_ Once the new population is created, it replaces the old population for the next iteration. _Termination:_ The cycle continues until the best fitness value falls below the defined threshold, indicating convergence to an optimum or near-optimum solution.
## 4 Numerical Results
### Effects of Grid Resolutions  
First, a grid-refinement study is conducted. Figure 10 shows the numerical results obtained from the RANS simulations using the four types of grids listed in Table 1. The Reynolds stress model is used as the turbulence model. The results are presented in terms of turbulent kinetic energy and vorticity magnitude.  
Given that the roll-up process of vortex formation involves a small-scale vortex structure and high gradient change near the vortex core, the grid-resolution is of critical importance to the accuracy of the numerical solutions. Table 3 compares the grid-resolution of different grid types in terms of the number of grids that are used to resolve the vortex core.  
The vortex core diameter, 0.0189 m, is computed using the distance between peak-to-peak of the tangential velocity profile obtained from the simulation result using the coarse grids. In the RANS simulation, the wing-tip vortex structure is dissipated rapidly along the downstream direction as the grid size increases. Hence, as the number of grids in the vortex region increases, the sharpness of the resolved vortex structure increases. Specifically, the vortex generated at the wing-tip is known to exhibit a partial laminar flow region in the vortex core due to the low rate of turbulence energy production. This fact can be confirmed in Figure 10 by identifying the vortex core region, where turbulent kinetic energy is low.  
Figure 11 compares the non-dimensional profiles of tangential velocity and pressure across the vortex core region at the position 0.5c downstream of the wing-tip. It is inferred that as the grid-resolution becomes dense, the predicted profiles of tangential velocity and pressure become steeper and converge to a single curve.  
Figure 9: Experimental setup; (**a**) for visualization; (**b**) for noise measurement.  
Figure 10: Turbulent kinetic energy and vorticity magnitude for cases using different types of grids; (**a**) test grids; (**b**) coarse grids; (**c**) middle grids; (**d**) dense grids.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Number of Grid Points across the Vortex Core**} \\ \hline
**Test Grids** & **Coarse Grids** & **Middle Grids** & **Dense Grids** \\ \hline
3.6 & 13.5 & 27 & 54 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of grid points across the vortex core to predict tip-vortex flow.
## 4 Conclusions  
In this study, a novel SMA-based idle air control actuator for automotive engines was proposed to improve the engine's idling conditions. The innovative idle air control actuator uses shape memory alloy (SMA) smart material as a new technology to activate and control the idling mode of the engine. The inherent characteristics of SMAs, particularly reversible phase transformations in response to thermal changes, were harnessed to create an IAC actuator that is not only simple in structure and compactness but also possesses energy efficiency actuation capabilities with only 2 A of supplied electric current. Through mathematical modelling in analytical analysis and numerical analysis using COMSOL Multiphysics software (R2023b), it was demonstrated that the SMA-based IAC actuator effectively modulates air intake during engine idle mode. This level of idling control contributes significantly to the overall idling efficiency of the engine, addressing key challenges in achieving good performance during idling conditions. The successful control strategy with the PID controller in SMA spring models further enhances the adaptability of the SMA-based IAC actuator's full opening state in 3 s and attaining the engine's idling speed of 743.8 RPM. The operability of the actuator at low stress within multiple cycles permits SMA material to be a viable and robust solution for the demanding conditions of real-world vehicle applications. Finally, the looking forward ongoing research will focus on refining the actuator's design with optimizing control not based on the existing actuators but on the engine's idling conditions, multiple experiments, and practical implementation of this actuator in a real spark ignition engine to validate the simulation results and address any potential challenges associated with its integration into diverse vehicle platforms.  
Conceptualization, P.T., J.W.S. and S.-B.C.; software, P.T.; formal analysis, P.T.; investigation, P.T.; writing--original draft preparation, P.T.; writing--review and editing, S.-B.C. and J.W.S.; supervision, J.W.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT, MSIT) (No. 2023 RIA2C1007973). This research was supported by the MSIT (Ministry of Science and ICT, MSIT), Korea, under the Grand Information Technology Research Center support program (IITP-2024-2020-0-01612) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).  
Data is contained within the article.
## 1 Introduction
This paper reports an empirical and conceptual investigation at the interface between wireless power transfer and CMOS neuron IC for retinal image acquisition for the first time. The proposed implementation includes a wireless antenna, RF power harvester, and retina neuron circuit. The on-wafer antenna and RF power harvester IC could be placed on contact lens, which delivers light source to the photoreceptor into retinal circuit. The work is organized as follows: Section 2 involves concepts of integration strategy, design, and performance evaluation of on-wafer antenna, RF power harvester circuit, and retina neuron IC. The result and discussion of CMOS neuron IC is given in Section 3, followed by conclusion in Section 4.
# Routing Attacks Detection in 6 LoWPAN-Based Internet of Things
## 4 Proposed Combination Approach for an IoT Intrusion Detection System
### Centralized IDS Deployment  
The IDS is situated either on a dedicated host, such as a cluster head node, or on the root node, such as a border router, and monitors the traffic passing through it to detect attacks. Typically, the central node of the IDS sends periodic requests to the monitored network for updates. This model collects data from different sources within the network and transmits them to a central location for analysis and correlation to identify potential security threats. This deployment model offers several advantages, such as enhanced visibility and control of network traffic, simplified management of security infrastructure, and centralized policy management. However, it also poses some challenges, such as the potential for a single point of failure and the need for a high-speed network connection to transmit data to the central system.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 2 The Development Status and Key Technology Of ISAC
### Prototypes Studies  
With the development of ISAC, multiple universities and companies have conducted hardware experiments on ISAC prototypes and gradually applied ISAC in practical scenarios; for example, China Telecom and ZTE have utilized ISAC to ensure low-altitude safety during the Asian Games [30]. These experiments involve the construction of practical systems to further test and optimize the performance of ISAC systems. They are conducted on the 6G ISAC prototype across various frequency bands and scenarios to validate the system performance with different metrics. To meet the performance requirements of ISAC, multiple research teams have initially built experimental ISAC systems.
## 4 Numerical Results
### Case II: Broadband Patch Antenna
Figure 15 shows the photographs of the antenna prototype, the reflection and realized gain characteristics, as well as the H- and E-plane radiation patterns. The agreement between the simulation and measurement data is satisfactory. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Training** \\ **Samples** \\ \end{tabular} } & \multicolumn{3}{c}{**Relative RMS Error**} & \multirow{2}{*}{\begin{tabular}{c} **Number** \\ **of** \\ **Training** \\ **X** \\ \end{tabular} } & \multicolumn{2}{c}{**Relative RMS Error**} \\ \cline{2-7}  & \begin{tabular}{c} **Conventional** \\ **Models (Domain** \\ **X**) \\ \end{tabular} & \begin{tabular}{c} **(Original) Nested Kriging** \\ **with Fixed Domain** \\ **Thickness** \\ **Thickness** \\ **Thickness** \\ \end{tabular} & \
\begin{tabular}{c} **of** \\ **Training** \\ **Samples** * \\ \end{tabular} &
\begin{tabular}{c} **Nested Kriging with** \\ **Variable-Thickness** \\ **Domain [this Work]** \\ \end{tabular} \\ \cline{2-7}  & **Kriging** & **RBF** & \(T_{\text{max}}\) **= 0.025** & \(T_{\text{max}}\) **= 0.05** & & \(T_{\text{max}}\) **= 0.025** & \(T_{\text{max}}\) **= 0.05** \\ \hline
50 & 43.4\% & 52.0\% & 10.1\% & 17.8\% & 30 & 13.0\% & 14.8\% \\
100 & 21.2\% & 25.4\% & 7.9\% & 9.0\% & 63 & 7.8\% & 9.1\% \\
200 & 16.0\% & 18.2\% & 7.2\% & 6.5\% & 125 & 7.3\% & 6.4\% \\
400 & 12.8\% & 14.7\% & 4.7\% & 6.1\% & 247 & 4.9\% & 5.3\% \\
800 & 9.8\% & 11.3\% & 3.3\% & 4.8\% & 505 & 3.8\% & 5.1\% \\ \hline \hline \end{tabular}  
* The number of training samples is determined by the relative volume of the variable-thickness and fixed-thickness domains (see the design of experiments procedure description at the end of Section 3).  
\end{table} 
Table 2: Modeling Results for Antenna of Figure 11.  
Figure 13: Patch antenna: reflection responses at the selected test designs: EM model (), nested kriging with variable-thickness domain obtained using 800 training samples and \(T_{\text{max}}\) = 0.05 (o). Figure 14: Optimization of the antenna of Figure 11 using the variable-thickness nested kriging surrogate set up using 800 samples (\(T_{\max}=0.05\)): initial design obtained from the first-level model (...), surrogate model response at the optimized design obtained using variable thickness model (- - -), response of the optimized fixed-thickness surrogate (o), EM simulated response at the variable-thickness surrogate model optimum (—): (**a**) \(f_{0}=4.8\) GHz, \(\varepsilon_{r}=3.38\), \(h=0.51\) mm, (**b**) \(f_{0}=3.8\) GHz, \(\varepsilon_{r}=2.5\), \(h=0.76\) mm, (**c**) \(f_{0}=5.3\) GHz, \(\varepsilon_{r}=3.38\), \(h=0.81\) mm, (**d**) \(f_{0}=5.3\) GHz, \(\varepsilon_{r}=4.4\), \(h=1.0\) mm. Figure 15: Antenna of Figure 11 optimized for \(f_{0}=4.8\) GHz, \(\varepsilon_{r}=3.38\) and \(h=0.51\) mm, implemented on RO4003C substrate: (**a**) antenna prototype, (**b**) reflection, (**c**) realized gain, (**d**) H-plane pattern at 4.8 GHz, (**e**) E-plane pattern at 4.8 GHz; simulations (gray) and measurements (black).
## 4 Gravity Method
### Gravity Data Acquisition and Reduction
#### 4.1.2 Gradient Analysis  
This method is used to determine geometric parameters, such as the boundary location and depth of the source body. Horizontal gradient analyses have been widely used to localize the lateral density contrast from gravity data. This analysis is less susceptible to artifact interference in the data because it only requires the calculation of the two first-orderhorizontal derivatives. This method is useful for enhancing both the shallow and deep sources [51]. The amplitude of the horizontal gradient is expressed as:  
\[HG_{g}=\sqrt{\left(\frac{\partial g}{\partial x}\right)^{2}+\left(\frac{\partial g }{\partial y}\right)^{2}}, \tag{9}\]  
where \(\left(\frac{\partial g}{\partial x}\right)\) and \(\left(\frac{\partial g}{\partial y}\right)\) are the horizontal derivatives of the gravitational field in the \(x\) and \(y\) directions, respectively.  
A vertical gradient analysis was used to localize the vertical density contrast boundary from the gravity data. The amplitude of the vertical gradient is expressed as:  
\[VG_{g}=\left(\frac{\partial g}{\partial z}\right). \tag{10}\]  
We used a second-derivative analysis (SVD) to distinguish the local geological features from the regional ones. An SVD image often provides a clearer and improved description of the anomaly, especially on its important exploration properties, compared with the CBA [52]. Moreover, this analysis is advantageous. It is highly sensitive to small errors in gravity measurements. An SVD analysis covers the calculation of \(\frac{\partial^{2}g}{\partial x^{2}}\) and \(\frac{\partial^{2}g}{\partial y^{2}}\) from the gravity field. From the Laplace equation, it has been proven that the sum of the horizontal gradient produces \(\frac{\partial^{2}g}{\partial x^{2}}\) with the opposite sign.
## 3 Spread-Spectrum Technology
### Direct-Sequence Spread-Spectrum  
The communication principles of the direct spread-spectrum system are shown in Figure 2. The transmitter spreads the signal to a wider frequency band, which is transmitted by carrier modulation and the channel, and the receiver performs the de-spreading of the received signal, restoring the broadband signal to a narrowband. The de-spread signal is then demodulated by the local carrier to restore the original information.
## 4 Discussion of Test Results
### Image Acquisition and Cracking Recognition
#### 4.3.3 Analysis of the Cracking Distribution in Depth Direction  
In order to study the cracking distribution along depth direction, binary images were processed by a user-defined code [26]. As for the logic of the code, white pixels which represents crack regions were all numbered with value \"1\", other regions were marked with value \"0\". Therefore, by calculating the summation of each image slice matrix, the size of the crack region in each depth section can be determined. The results are shown in Figure 17.  
Figure 16: Crack recognition by processing computed tomography (CT) image slices.  
Figure 17: Crack pixel number variation along specimen depth direction.  
As shown in Figure 17, the number of cracking pixels decreased with the increase of depth. In the No.1 specimen, the fatigue crack disappeared completely below 20 mm depth. In the No.2 specimen, the depth at which the crack completely disappeared was 22 mm. Although cracks decreased as depth increased, there were still local fluctuations. For example, there were 6616 pixels at the surface of the No.1 specimen, but the pixel number on the adjacent section was 6690. This phenomenon indicates that the cracks in ECC specimens were not all propagated top-to-bottom. Some cracks might have been caused by the internal damage during the fatigue cracking process.  
By comparing these two specimens, it could be seen that there were more cracks close to the top part of No. 1 specimen, while there were more cracks below a depth of 9 mm in the No. 2 specimen. By summing all the crack pixels in all of the image slices, the crack pixel amount in the No. 1 specimen was 91,821, while the amount in the No. 2 specimen was 91,125, as shown in Figure 18. These two values are very close, and the variation coefficient was only 0.538%. This phenomenon indicates that the fatigue test of the ECC pavement based on actual working conditions is reliable and effective. It is a scientific and reasonable technical means to identify cracks and analyze crack regions using DIP technology, and the crack image pixel statistical method was also a valuable way to evaluate the fatigue crack distribution in ECC.  
Furthermore, more replicates needed to be scanned and analyzed to verify the reliability of the results on the cracking distribution. Also, based on the limited samples, this could hardly explain the mechanism of the cracking propagation of this fiber concrete within the OSBD pavement structure, so more samples with a different mix of design parameters, including fiber contents, mix methods and so on, need to be investigated.
## 3 Results and Discussion
### Antioxidant Potential  
The seven mushrooms showed remarkable antioxidant potential (Figure 7). _A. jacksonii_ was the source with the highest phenolic content (>100 mg 100 g\({}^{-1}\) DW), whereas _R. delica_ and _R. brevipes_ showed similar levels (<100 mg 100 g\({}^{-1}\) DW) (Figure 7a). Such activity was comparable to that found in native \"quelites\" consumed in this geographical region [11]. Interestingly, there was no correlation with the antioxidant capacity since _L. indigo_ and _R. delica_ showed the highest antioxidant capacity without statistically significant differences between them. According to our results, the edible mushroom with the lowest phenol content was _C. subclavipes_, but its antioxidant capacity surpassed 200 uM/g TEAC. This trend strongly suggests that other compounds with different structures to phenolics may influence divergences in the total antioxidant capacity. As is known, edible mushrooms contain at least 1000 metabolites of different chemical natures and many of them could exert antioxidant activity [5]. Considering that the present investigation was limited to determining specific phenols and fatty acids with proven nutraceutical properties, the synergistic antioxidant activity of other compounds accumulated in the edible mushroom cannot be discarded. Remarkably, the total phenol content of the seven edible mushrooms consumed in the NHP was higher than those consumed in other regions of Mexico [31]. According to our results, these fungi presented three to fourfold higher levels of phenols than _A. bisporus_, _Macrolepiota procera_, and _Boletus edulis_ commercialized and consumed in  
Figure 6: Contents of _p_-hydroxybenzoic acid (**a**), _p_-cumaric acid (**b**), protocatechuic acid (**c**), and cinnamic acid (**d**) in seven edible mushrooms consumed in the northeastern highlands of Puebla Mexico. 1, _Lactarius indigo_. 2, _Clitocyhe nuda_. 3, _Clitocyhe subclavipes_. 4, _Russula delica_. 5, _Russula brevipes_. 6, _Clitocyhe squamous_. 7, _Ammita jacksonii_. Different letters indicate means (_n_ = 10) with statistically significant differences by ANOVA-Tukey (_p_ < 0.05) and spaces without bars indicate non-detectable compounds under assayed conditions. Raw data are presented in Table S4. The concentrations are presented in dry weight.  
Chihuahua, Mexico [31]. Similarly, the antioxidant potential of the seven edible mushrooms consumed in the NHP was evidently higher than that reported for _Russula integra_, _R. nigricians_, _R. vesca_, and _Sarcodon imbricatus_ consumed in Tlaxcala, Mexico [32].
# Energy Efficient IoT Data Collection in Smart Cities Exploiting D2D Communications
## 4 The Novel D2D-Based Energy Efficient IoT Data Collection
### Assumptions for the D2D-EE Algorithm Implementation  
For the proposed D2D cluster-based IoT data collection, we assume a network-assisted D2D communications where the eNodeB knows the current network state and is able to implement the proposed D2D-based IoT data collection. In particular, we consider scenarios where the IoT devices have very small computation capabilities compared to the LTE eNodeB. The eNodeB will be responsible for the allocation of the available radio resources to the cluster head(s) in the network, the so-called _aggregators_ that will be in charge of uploading all IoT data from the cluster to the eNodeB. On the other hand, a completely distributed approach would require high signaling overhead for information sharing among the objects to build a shared knowledge of the network topology and the relevant channel quality information. Then, with the aim of avoiding any interference between transmissions occurring in different clusters, we assume a Round Robin radio resource allocation policy over the aggregators in the cell. In fact, this guarantees that orthogonal frequencies are used in any transmission and interference is avoided. However, enhanced radio resource management policies may be implemented for improved interference management and performance. Also for the intra-cluster D2D communications, radio resources are allocated according to a Round Robin policy, where the set of available resources is the set of resources allocated to the respective aggregator. In this way we avoid any interference within a single cluster as well. For the D2D communications we foresee a decode-and-forward (DF) relaying configuration operating in half-duplex TDD mode. First, all the data from the cluster is received by the cluster aggregator; then, the aggregator will forward all aggregated data to the eNodeB. Thus, cellular mode and D2D transmissions will never occur in the same TTI (recall that uplink resources are used for the D2D transmissions) and consequently we can assume no interference is to be managed among cellular and D2D links within a cluster.