esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Environmental Results  
This study aimed to measure the amount of CO\({}_{2}\) emissions that may be reduced by installing PV systems for charging EVs. The theory posited that the increased adoption rates of EVs, in conjunction with the utilization of PV systems, would result in substantial reductions in CO\({}_{2}\) emissions. This hypothesis was evaluated in two scenarios: one where 80% and the second were 20% of the energy is powered by the PV system. The calculation of the annual CO\({}_{2}\) emissions savings from the integration of PV charging for EVs is depicted in the provided flowchart (see Figure 8). This schematic provides a clear overview of the equation used in the study, delineating each parameter's role and how they collectively determine the CO\({}_{2}\) reduction potential.  
The potential CO\({}_{2}\) reduction is depicted in Figure 9 as a function of the number of EVs and their energy consumption rates (R) by use of PV-EVCS. The CO\({}_{2}\) savings increase with the percentage of PV in the EVCS (80%), for all the EV adoption levels, ranging from 1000 to 50,000 vehicles. It is noteworthy that the growth in CO\({}_{2}\) reductions is linear with  
Figure 8: Summary of CO\({}_{2}\) emission calculations.  
Figure 7: Accrued cash flow (cash balance).  
the number of EVs, regardless of the PV-EVCS share. This highlights how crucial it is to increase the efficiency of EVs and the proportion of PV in the infrastructure used for charging them to maximize the reduction of CO2 emissions. The yearly reduction in CO2 emissions was computed for various EVs (ranging from 1000 to 50,000 cars) with variable rates of R (0.15 to 0.30 kWh/km) in MtCO2/year, see Table 16.
## 1 Introduction
Thus, as WSNs become more pervasive and application areas continue to grow exponentially, the need to build OVWSNs over existing infrastructures will undoubtedly improve the implementation of resilient networks that will meet the future demands of WSN. Thus, following the growth of OVWSN [12; 13], including the increased rate of published materials in this regard, it has become necessary to provide an overview of the progress made thus far in this research area. Therefore, this paper provides a detailed review of OVWSNs, beginning with the motivation behind the need for virtualization in WSN. Then, different existing challenges are provided with an emphasis on virtualization as a possible solution. This is followed by an overview of VWSNs, its building blocks and the different types of virtualization platforms and how they comparatively perform. An exposition into OVWSN is provided based on different areas such as its topology, routing, media access and its service delivery. The state-of-the-art with regards to OVWSN is discussed. After that, few design requirements towards improving OVWSN are considered, followed by the presentation of some existing research challenges for future works. By the above, the following contributions are made in this paper:  
* A comprehensive comparison of the different techniques used for implementing VWSN is provided under different categories including the various operating systems used, the middleware, and also the virtual machines used. * The concept of OVWSN is thoroughly discussed as a potential solution to some of the problems encountered in smart industrial applications. * Peer-to-peer topologies are well contrasted with ring topologies mainly as possible enablers for OVWSN. * Some research challenges and potential solutions in OVWSN are also discussed for easy comprehension by budding researchers in the OVWSN study area. * A conceptualization of the design requirements for future OVWSN has also been provided and discussed. The rest of this paper is presented as follows: Section 2 expounds on the concepts of virtualization in WSN, and an application scenario of a smart industrial application [5] is presented. Section 3 discusses the importance of shared sensors as fundamental to the emergence of virtualized WSN. Section 4 details the core of the article on overlay virtual wireless sensor networks, while Section 5 discusses the design requirements for overlay virtual WSNs. Section 6 presents the open research areas available, and Section 7 concludes the article.
# MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design
## 5 Conclusions  
In this paper, a modified optimization algorithm known as MAOA is implemented for solving the problem of antenna array pattern synthesis with different objectives. The proposed algorithm efficiently handles the problem and, in turn, generates the optimal values of position and current amplitude for various designs of an LAA. High performance level is offered in PSLL reduction, CSLL minimization, SLL minimization with and without beamwidth constraint also helps in controlling the direction of the nulls. The result thus obtained is benchmarked with other well-known intelligent optimization algorithms, and MAOA outperforms in every aspect for synthesizing the LAA of different designs. One design of CAA is also considered in this research, and the simulation result shows that MAOA-optimized array outperforms UCA and PSO-optimized antenna array. The simulation results reveal that MAOA handles the optimization problem of the antenna array in a much convenient manner and can also be applied for any electromagnetic engineering problem. In addition to this, a statistical test called Wilcoxon rank sum test and Friedman rank are also performed for checking the authenticity of the proposed algorithm, the test results also point that MAOA is an efficient technique for the said problem of pattern synthesis. Based on its performance, this algorithm equally applied to synthesize other antenna geometries like concentric, elliptical, planar, and conformal antenna arrays.  
Conceptualization, A.R., A.S. ([PERSON]) and W.H.L.; methodology, A.S. ([PERSON]), A.S. ([PERSON]), A.K.A., R.S. and W.H.L.; simulation, A.R. and A.S. ([PERSON]); validation, A.R. and A.K.A.; formal analysis, A.R. and W.H.L.; investigation, A.R. and A.S. ([PERSON]); resources, W.H.L. and A.S. ([PERSON]); data curation, A.R., K.S.C. and S.S.T.; writing--original draft preparation, A.R.; writing--review and editing, A.S. ([PERSON]), W.H.L., K.S.C. and S.S.T.; visualization, A.R. and R.S.; supervision, A.S. ([PERSON]); project administration, A.S. ([PERSON]); funding acquisition, K.S.C., S.S.T. and W.H.L. All authors have read and agreed to the published version of the manuscript.  
The study was funded by the Malaysian Ministry of Higher Education through the Fundamental Research Grant Scheme (FRGS/1/2024/TK07/UCSI/02/1).  
Figure 26: Friedman ranking of optimization algorithms used for amplitude optimization of LAA.
# Alginate Modification and Lectin-Conjugation Approach to Synthesize the Mucoadhesive Matrix
## 4 Alginate Modification and Coupling Strategies
### Carboxylic Groups
#### 4.1.1 Carbodiimide  
Carbodiimide is a widely known method to modify alignates that converts the carboxylates into ester and amide derivatives. The technique utilized a particular ratio of 1-ethyl-3-(3-dimethyl aminopropyl) carbodiimide (EDC) with N-hydroxysuccinimide (NHS) or hydrochloride (HCl). Alginate modifications with bioactive peptides for tissue engineering, for example, are usually achieved by carbodiimide chemistry. Using carbodiimide chemistry, the manuronan part of alginate is modified up to 0.2% degree of substitution by peptide [60].  
The synthesis of gelatin-alginate tissue adhesive is synthesized using this methodology [61]. The resulting polymer is suitable as a bioadhesive to bind soft tissue and locally deliver an antibiotic drug with good cytotoxicity results [62]. Others have also reported the application of carbodiimide chemistry to synthesized dodecanol amphiphilic alginate as an emulsifier and hydrogels of tyramine-alginate for bioactive scaffolds, and for creating cell-laden bioinks from thiol-ene crosslinked alginate [63; 64; 65; 66].  
The general crosslinking mechanism of carbodiimide chemistry is depicted in Figure 3. The EDC/NHS coupling mechanism is associated with urea derivatives in the first step of the reaction [2]. In some cases, this by-product formation is a disadvantage and has contributed to the destruction of the gelling properties of alginate derivatives [67].
## 2 Methods
### Patch Antenna with Size Reduction  
Size reduction methods for patch antennas have been actively researched [26; 27; 28; 29; 30]. One of the most common methods to reduce the size of the antennas is to use meander patterns [30]. As described in the introduction, the resonant modes of the patch antenna are related to the frequency and size of the antenna. Thus, we propose reducing the size of the antenna using the effective length and width of the antenna [24; 25; 31], by folding the antenna in a zigzag manner. The width of the antenna is reduced by meandering the ground plane in a number of steps (\(n\)) in the horizontal direction so that the plane consists of two horizontal and two vertical lines. The height of each step is \(lx\), and the length of each step is \(lx\) such that the effective length can be described by the following equation:  
\[W=2n(lx+lx) \tag{1}\]  
Figure 2 shows the application of this equation and the meandering method. By choosing \(n=5\) and \(lx=10\) mm, \(lx\) becomes 18.76 mm, which makes the total width of the antenna 187.6 mm. A similar equation and method can be used to reduce the length of the antenna, and in this work, we applied meandering on the patch plane in the vertical direction. A non-uniform meander patter will allow us to design the antenna to have a straight patch plane at the center, as to uninterrupted conductor area closer to the phantom or the rat so that the field can be of higher intensity. To reduce the length of the antenna, we opted to have a non-uniform meander pattern on the patch plane.  
Figure 1: Design of (**a**) a patch antenna without size reduction, (**b**) S\({}_{11}\) parameters of the antenna showing dual frequency, (**c**) the \(\mid\)B\({}_{1}\)\(\mid\)-field of the antenna at 300 MHz and (**d**) the \(\mid\)E\(\mid\)-field at 429 MHz.  
We further reduced the size of the antenna by applying a number of steps \(n=10\) in the horizontal direction and \(n=3\) in the vertical direction, for the ground and patch plane, respectively. The size of the horizontal step \(hx\) was 10 mm and \(lx\) was 4.5 mm, the vertical \(hy\) was set to 15 mm and \(ly\) was 32.5, and 20 mm for the small steps and 50 mm for the larger plane in the middle of the antenna. Figure 3 shows the final geometry. The height, width and length of the dielectric material were 30, 99 and 150 mm, respectively. In Figure 3b the ground plane is shown with the corresponding meandering pattern, and Figure 3c for the top plane of the patch.
## 3 Converter Topology for the Power Modules
### Single Input Single Output Mode  
The converter is able to work in single input single output (SISO) mode when there is no power available from the PV-module (shading condition), therefore all the load power has to be supplied by the battery module. As the PV-converter is not in operation, the proposed converter operation during this mode is determined by the battery converter which operates as a normal buck converter and its output voltage is determined by Equation (13) in the same way as for DISO mode.  
As can be observed from analyzing the different modes of operation, the proposed converter output voltage range is determined by the mode of operation as follows:  
During SIDO mode which covers the charging of the battery from the PV: based on Equation (11) the proposed converter works in buck-boost operation therefore the theoretical output voltage ranges from 0 V to voltages higher than the battery voltage (V\({}_{\text{batt}}\)).  
During DISO and SISO modes which only cover the discharge of the battery, based on Equation (13), the theoretical output voltage ranges from 0 V up to V\({}_{\text{batt}}\) (i.e., the output voltage needs to be smaller than the battery voltage).  
Accordingly, and in order to ensure the proper operation of the proposed converter under all modes of operation, the voltage of the battery module should be selected such that: the minimum voltage of the battery module (at minimum SoC) should be higher than the maximum targeted output voltage. On the other hand, in order to ensure continuous extraction of maximum power available from the PV modules, the voltage of the PV-module should be selected such that: the maximum voltage of the PV-module should be lower than the minimum targeted output voltage. This will be discussed in more details in Section 4.
# Random Noise Suppression Method of Micro-Seismic Data Based on CEEMDAN-FE-TFPF
## 2 Materials and Methods
### Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)  
By adding a finite amount of adaptive white noise in each stage, CEEMDAN can realize that the reconstruction error approaches zero under a small average number of times. This solves the problem of modal aliasing and avoids the distortion of the original signal. The steps are as follows.  
1. The adaptive white noise is added to the original data \(S(t)\) several times, respectively. In this paper, \(q=50\) is taken, and the \(q\)-th signal is \[S_{q}(t)=S(t)+\sigma_{q}B_{q}(t)\ (q=1,2,\cdots,\ 50) \tag{1}\]  
where \(\sigma_{q}\) means the standard deviation of the adaptive noise for the \(q\)-th time.  
Then, the first IMF decomposed by CEEMDAN can be expressed as  
\[IMF_{1}=\frac{1}{q}\sum_{i=1}^{q}IMF_{1}^{(i)} \tag{2}\]  
where \(IMF_{1}\) means the first IMF of \(S_{q}(t)\).  
(2) Obtain the residual of the first IMF:  
\[R_{1}(t)=S(t)-IMF_{1} \tag{3}\]  
(3) The adaptive white noise is added to the residual of the first IMF several times for constructing a new signal to be decomposed:  
\[S(t)=R_{1}(t)+\sigma_{q}B_{q}(t) \tag{4}\]  
The second IMF of the original signal can be expressed as  
\[IMF_{2}=\frac{1}{q}\sum_{i=1}^{q}IMF_{1}^{(i)} \tag{5}\]  
(4) Repeat the above operation until the original signal is completely decomposed, resulting in a total of \(\omega\) IMFs. The last residual is calculated as  
\[R=S(t)-\sum_{p=1}^{\omega}IMF_{p} \tag{6}\]
## 3 Experimental Results
### Analysis of the Test Results
#### 3.2.5 In-Plane Deflections and Rotation Angle at the Support Axis  
Figure 20 shows the curve of the in-plane (horizontal) deflections with time. The negative deflections indicate that the deflections of the slab develop outward along the axis, and the positive deflections indicate that the deflection develops inward along the axis. When the fire stopped, slabs CS1 and CS2 expanded 20.36 mm and 17.92 mm along the long side and 2.39 mm and 0.02 mm along the short side. After the test, due to the decrease in the furnace temperature, the effect of thermal expansion was reduced, and H1 and H2 moved to the plate and were still in the expansion state. H3 moved slightly to the inside and then returned to the outside, and H4 moved slightly to the outside and then returned to the inside.  
Figure 21 shows the curve of the rotation angle at the supporting axis of the concrete slab with time. The deflection deformation in the slab leads to its rotation along the support axis; there is a clamped end constraint at the clamped end, which has a certain inhibition effect on the rotation of the edge of the clamped plate, and rotation angle of R4 far less than that of the three simply supported edges. There are no constraints on the long simply supported edge and the two plate angles, so rotation of R3 is the largest; the short simply supported edge has a single plate angle constraint, and the degree of rotation is in the middle compared to the rotation for other constraints.  
Figure 19: Curves of the vertical out-of-plane deflection at the middle of the edge of the slab versus time.  
Figure 20: Curves of the horizontal deflections versus time.
# Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification
## 1 Introduction
Motivated by such a consideration, we propose a multiscale superpixelwise LPP (MSuperLPP) method for HSI classification in this work. The method is able to deal with spatially local homogeneities in HSIs during dimensionality reduction, thus offering the potential to improving the subsequent classification. To the best of our knowledge, there is no similar work to ours in existing literature, which performs local homogeneity manifold-based HSI dimensionality reduction. Our methods comprise three major phases. First, we segment an HSI into many homogeneous subregions using entropy rate superpixel segmentation (ERS) with a series of scales, which can exploit fully rich spatial dependencies in different shapes and sizes. Next, on each scale, LPP is run on each subregion with spectral-spatial covariance feature, and the obtained low-dimensional features are fed into a preliminary classifier. In the final step, the results on all the scales are aggregated with a decision fusion strategy to yield the final classification result. The remainder of this paper is organized as follows. In Section 2, we introduce some backgrounds related to the proposed approach. Section 3 gives the details of the proposed MSuperLPP. Section 4 presents the experiment results, and Section 5 provides a summary of this paper.
## 3 The Model Description and Data Preprocessing
### RNN Models
#### 3.1.2 Gated Recurrent Unit (GRU)  
As is shown in Figure 2, a layer of GRU is defined by Equations (7)-(10) as follows:  
\[r_{t}=\text{sigmoid}(W_{t}x_{t}+U_{t}h_{t-1}+b_{r}) \tag{7}\]  
\[u_{t}=\text{sigmoid}(W_{u}x_{t}+U_{u}h_{t-1}+b_{u}) \tag{8}\]  
\[\widetilde{h_{t}}=\text{tanh}\big{(}W_{gx}x_{t}+U_{gx}(r_{t}\circ h_{t-1})+b_ {gx}\big{)} \tag{9}\]  
\[h_{t}=u_{t}\circ h_{t-1}+(1-u_{t})\circ\widetilde{h_{t}} \tag{10}\]  
where \(r_{t},\ u_{t}\) are outputs from reset gate and update gate. The reset gate controls how much the previous hidden state \(h_{t-1}\) is considered to create a new hidden state \(\widetilde{h_{t}}\), using the gate output \(r_{t}\ (0<r_{t}<1)\) which is the output from Equation (7). Similarly, the update gate is in control of deciding how much the new hidden state \(\widetilde{h_{t}}\) is mixed to generate the next hidden state \(h_{t}\), using the gate output \(u_{t}\ (0<u_{t}<1)\) which is the output from Equation (8) [4]. The performance of GRU compared to that of LSTM depends on the learning task, while the required number of weight parameters for the same number of units is 3/4 that of LSTM.
## 2 Results and Discussion
### Identification and Quantification of Key Aroma Compounds of 15 Brands of EVOOs
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**Ion Selection**} & \multirow{2}{*}{**Standard**} & \multirow{2}{*}{**\(R^{2}\)**} & \multirow{2}{*}{**\(R^{2}\)**} & \multicolumn{6}{c}{**Concentration (mg/s)**} \\ \cline{4-19}  & & & & & & & & & & & & & & & & & & & &  
As shown in Table 4, (Z)-3-hexenyl acetate showed the highest odor activity value (OAV), indicating the strong contribution to the flavor of EVOOs. (_E_)-2-heptenal, hexanal, acetic acid, and (_E_)-2-hexenal were also identified as the key aroma compounds based on OAVs by SPME. It was in accordance with the results by SAFE-AEDA. While (E)-3-hexenol and 6-methyl-5-hepten-2-one had little aroma contribution due to the lower OAVs, there were some differences between the results based on AEDA and OAV. Hence, the key aroma compounds could be identified precisely by both methods.
## 3 Results and Discussion  
Comparison of the results in Figure 5a,b provides an insight into the performance of the PM demodulator depending on the phase deviation value \(\Delta\varphi=\pi/2\) in (a) as opposed to \(\Delta\varphi=\pi/3\) in (b), for the same SNR = 20 dB. The following subfigures (c) to (f) maintain the same phase deviation \(\Delta\varphi=\pi/3\) that yielded proper noise stability results of the PM demodulator, while varying the SNR, and in this case, an adequate comparison between various demodulator types can be carried out. Therefore:  
* Emission classes that use phase modulation have been shown to be more resistant to interference when a \(\pi/3\) phase deviation has been used, which is much more resistant than those that use frequency modulation.
* For emission classes using frequency modulation, at SNR values below 10 dB (considered to be the full improvement threshold) the actual signal shape can no longer be recognized--the effect of signal captured by noise occurs.
* Emission classes using DSB AM showed similar resistance to disturbance using SSB AM, so that, for both, the useful signal can be recognized even in the conditions of an SNR of \(-3\) dB.  
When comparing the simulation results in Figure 5 to those obtained analytically and illustrated in Figure 3, it is noted that the demodulating systems ranking from the perspective of noise stability is similar only for the situation of a high SNR = 20 dB (Figure 5b), and for small values (below 1) of the corresponding dependence parameters \(m\), \(K\), \(D\) in Figure 3. In this case, the J3E system (SSB AM) yields the highest stability in terms of both simulation results and IFD value. However, there is a notable difference for the DSB AM system (A3X class) compared to the FM system (F1B class): although the simulation in Figure 5 indicates a low performance of the F1B class system compared to the A3X one, the analytical calculations give high IFD values for the F1B, regardless of the normalized modulator signal power, the modulation index \(m\), or the frequency deviation \(D\). G3E (PM) systems yield the lowest performance in both cases.  
Table 1 presents a comparative situation between the results obtained by the two methods adopted and the conclusions formulated in the specialized technical literature and the bibliographic references.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Stability to Disturbances**} \\ \cline{3-5}