esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design and Optimization  
This section covers the design and optimization of the single element antenna followed by detail analysis of multi-element array designs in order to obtain the required performance matrices for the targeted 5G application. The top and bottom layers of the antenna structures are made of conducting metal, such as copper. The substrate material used in the designs is a 0.787 mm thicker Rogers 5880, which has a relative permittivity (\(\epsilon_{r}\)) and loss tangent (\(\delta\)) of 2.2 and 0.0009, respectively.
# Influence of Yeast Strain on Odor-Active Compounds in Fiano Wine
## 3 Results and Discussion
### Gas Chromatography-Olfactometry Analysis  
To better understand the interaction among those volatiles and the potential resulting impact to the consumer, Aroma Extract Dilution Analysis (AEDA) was used to screen for those volatiles with an odor impact. The results of the olfactometric analysis carried out by AEDA are reported in Table 2.  
This approach offers a closer understanding of the potential sensory impact of each volatile compound, as it is widely known that the concentration of volatile compounds does not directly link to their odor impact due to multiple factors, such as their odor activity value as well as competition with other volatiles and the matrix. GC/O analysis revealed 43 odorous compounds in Fiano wines but only 28 of them were identified based on the comparison of their chromatographic profile with pure standards and GC/MS analysis.
## 5 Conclusions
### Educational Implications  
The findings of this study have significant implications for educational policy, especially concerning university admissions, curriculum development, and student support services. First, the strong link between Grade 12 mathematics performance and success in quantitative fields like Actuarial Science suggests that institutions should prioritise mathematics skills in their admissions criteria or introduce bridging programmes for under-prepared students. This would help align student readiness with the demands of rigorous programmes. However, the relatively weak and uncertain relationship between AP scores and final marks highlights critical considerations for admissions policies. Institutions that rely heavily on high AP scores as a prerequisite may overlook other predictors of academic success, such as non-cognitive skills, personal motivation, and resilience. This calls for a re-evaluation of admissions processes to adopt a more holistic approach. Incorporating additional assessments or metrics could help identify students with the potential to succeed in demanding environments beyond their prior academic achievements. Furthermore, the limited differentiation in performance among students admitted with high AP scores suggests that universities should provide tailored academic support and resources to help all students thrive, regardless of their initial admissions profiles.  
The disparities in academic performance based on socioeconomic status also highlight the need for policies that address inequality, such as improving access to quality education in under-resourced schools and expanding financial aid and mentorship programmes for disadvantaged students. This would ensure equal opportunities for success regardless of background. Additionally, policies should emphasise continuous student engagement through strategies like formative assessments, peer tutoring, and academic advising. Expanding access to support services, such as academic counselling and study skills workshops, would help students stay on track. Finally, recognising non-academic factors like personal motivation, study habits, and external support systems is crucial. A holistic approach, including mentorship and mental health support, would better address the diverse needs of students in demanding academic environments.
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 2 Video Deepfake Detection with CNN-LSTM
### Deepfake Creation and Detection
Through plotting key attributes, the model can detect inconsistencies, both in a single frame if the landmarks are far off and using a sequence of frames when monitoring how consistently facial landmarks change during a video. Another method of deepfake detection is monitoring the consistency of the audio to the visual cues from the lips by detecting if the mouth is open or closed when the audio occurs [12] or capturing inconsistencies such as a lack of lip synchronisation to determine the audio-visual dissonance over a video [13]. However, this could falsely mislabel a real video with audio corruption or poor quality. A common method to detect deepfakes is using spatial-temporal analysis, which uses data characterised by space and time to analyse data frame legitimacy. Deepfakes lack temporal coherence as frame manipulation produces low-level generated artefacts [14], referring to original artefacts created to look like real video frames. Although the generative method of the deepfake impacts its granularity, this method can detect details a human eye cannot see. A common spatial-temporal approach to deepfake detection is CNN with LSTM, where facial landmarks are mapped and sequential frames are captured to follow features iterating over the time-space domain to extract inconsistencies [15].
## 3 Benefits of an Optimized Enterprise Network System
### Effective Cloud Integration  
With the increasing development and delivery of data and applications across several public clouds, enterprise networks facilitate efficient communication between cloud-based applications and end users [58]. Enterprises anywhere can benefit from improved connectivity through cloud network integration, which will enable them to efficiently manage their operations and clients [59]. Additionally, it streamlines the real-time access to information and interaction between end users, internal staff, external stakeholders, and the network infrastructure.
# Biological Properties of Sandalwood Oil and Microbial Synthesis of Its Major Sesquietrepenoids
## 3 Biosynthesis of Santalene, Santalol, and Other Terpenoids Found in Sandalwood Oil
In _Santalum_ species, santalene/bergamotene synthases play a crucial role in converting (E, E)-FPP into a mixture of sesquiterpenes, referring to \(\alpha\) and \(\beta\) santalene, _epi_-\(\beta\)-santalene, and \(\alpha\)_-exo_-bergamotene [60; 61; 64]. On the other hand, ten cytochrome P450 enzymes from the CYP76F subfamily are essential in converting these sesquiterpenes into corresponding sesquiterpene alcohols, referring to santalol and bergamotol [65; 66]. Nine functionally characterized SaCYP76F enzymes belonging to the CYP76F subfamily have been identified as primarily producing (E)-\(\alpha\)-santalol, (E)-\(\beta\)-santalol, (E)-_epi_-\(\beta\)-santalol, and (E)-\(\alpha\)_-exo_-bergamotol, with a lesser amount of (Z) stereoisomers which are responsible for the aromatic flavor of sandalwood oil [65]. Among these enzymes, the CYP76F39v1 variant demonstrated the highest catalytic efficiency in producing (E) stereoisomers [67]. Recent research has shown that (Z)-\(\alpha\)/\(\beta\)-santalol and (Z)-\(\alpha\)-_exo_-bergamotol can be selectively synthesized _in vitro_ by the CYP736A167 enzyme from _S. album_[12].
## 3 Results and Discussion  
### Physical Charanzation  
The crystalline structure of MnO\({}_{2}\) nanoflowers was characterized by XRD. As presented in Figure 1, sharp diffraction peaks were observed at 20 of 12.94\({}^{\circ}\), 18.34\({}^{\circ}\), 28.78\({}^{\circ}\), 37.66\({}^{\circ}\), 42.14\({}^{\circ}\), 49.90\({}^{\circ}\), 56.44\({}^{\circ}\), 60.26\({}^{\circ}\), 69.74\({}^{\circ}\), 71.34\({}^{\circ}\), and 73.72\({}^{\circ}\), which can be well-indexed into (110), (200), (310), (211), (301), (411), (600), (521), (541), (222), and (730) facets, respectively. It is in good agreement with XRD standard card JSPDF 44-0141 [16; 17], suggesting tetragonal crystalline of \(\upalpha\)-MnO\({}_{2}\) were successfully synthesized. Moreover, no visible peak relating to impurities appears, indicating high-purity of \(\upalpha\)-MnO\({}_{2}\). SEM images of MnO\({}_{2}\)NFs are shown in Figure 2A, B. Obviously, flower-like nanostructures composed of interconnected nanoflakes suggests MnO\({}_{2}\) nanoflowers were successfully synthesized. The porous microstructures indicate that MnO\({}_{2}\) nanoflowers have a large specific surface area, which is favorable for electrochemical sensing. As shown in Figure 2C,D, the NrGO nanosheets were warped on the surface of MnO\({}_{2}\)NFs, suggesting MnO\({}_{2}\)NFs/NrGO nanocomposites were successfully prepared.  
Figure 1: X-ray diffraction (XRD) patterns of MnO\({}_{2}\) nanoflowers and standard card JSPDF 44-0141.
## 3 Results  
The geometric discrepancies between the estimated and actual positions of the implant in each group are shown in Table 1. In general, the linear, vertical, and angular deviations were largest in the MC group, followed by slightly smaller deviations in the MM group and the AS group. The ANOVA results showed that all deviations were markedly smaller in the AS group and in the MC group (\(p<0.001\)). Regarding the linear discrepancy, the linear deviation at the bottom of the MC group was the largest (\(0.58\pm 0.15\) mm), whereas the vertical deviation of the AS group was the smallest (\(0.04\pm 0.02\) mm). Post-hoc analysis revealed that the linear deviations at the bottom were not statistically different  
Figure 4: Reference image of the implant position.  
Figure 5: Discrepancy parameters between the estimated and actual 3D positions of implant. A: Linear deviation at the platform; B: linear deviation at the bottom; C: vertical deviation; D: angular deviation.  
between the MM and AS groups. In the vertical and angular deviations, there were no statistical differences apparent between the MC and MM groups.  
In terms of measurement parameters, the linear deviations were larger at the bottom than at the platform level in the MC and AS groups but not in the MM group. Scatterplots of the distribution of the measurement values show that the interoperator variability was smallest in the AS group for all deviation parameters (Figure 6). Conversely, the variability of measurement values among operators was larger in both the MC and MM groups, showing a wide distribution of data points.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Measurement Parameter**} & \multicolumn{4}{c}{**Group for Each Implant Detection Method**} \\ \cline{2-5}  & **MC** & **MM** & **AS** & **\(p\)-Value** \\ \hline Linear deviation at the platform (mm) & 0.42 \(\pm\) 0.17 \({}^{\text{a}}\) & 0.21 \(\pm\) 0.13 \({}^{\text{b}}\) & 0.05 \(\pm\) 0.03 \({}^{\text{c}}\) & \textless{}0.001 \\ Linear deviation at the bottom (mm) & 0.58 \(\pm\) 0.15 \({}^{\text{a}}\) & 0.15 \(\pm\) 0.11 \({}^{\text{b}}\) & 0.08 \(\pm\) 0.05 \({}^{\text{b}}\) & \textless{}0.001 \\ Vertical deviation (mm) & 0.24 \(\pm\) 0.15 \({}^{\text{a}}\) & 0.17 \(\pm\) 0.15 \({}^{\text{a}}\) & 0.04 \(\pm\) 0.02 \({}^{\text{b}}\) & \textless{}0.001 \\ Angular deviation (degrees) & 1.2 \(\pm\) 1.4 \({}^{\text{a}}\) & 0.8 \(\pm\) 0.8 \({}^{\text{a}}\) & 0.2 \(\pm\) 0.1 \({}^{\text{b}}\) & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of geometric discrepancy between the estimated and actual positions of the implant using different detection methods.  
Figure 6: Scatterplot of deviations among the different methods used for implant detection: (**a**) linear deviations at the platform; (**b**) linear deviations at the bottom; (**c**) vertical deviations; (**d**) angular deviations.
# Effect of Temperature on Channel Compensation in Optical Camera Communication
## 4 Channel Compensation in Optical Camera Communication  
Channel compensation is usually a two-stage procedure. During the first stage, the CSI of the OCC channel is obtained in order to form the channel matrix. In this work, the CSI is derived from the contributions of each emitted spectrum to the three wavelength-divided receivers (\(S_{px}^{(i,j)}\) terms). It has been assumed that the link establishment procedure includes a training sequence in which each emitter is turned on sequentially in a Time-Division-Multiple-Access (TDMA) scheme (as in [10]). The elements of this matrix, named \(\mathbf{H}\) hereinafter, respond to Equation (8).  
\[h_{ij}=\begin{cases}K_{cam}\left(S_{px}^{(i,j)}T_{exp}+N\right)&\text{ if }S_{px}^{(i,j)}T_{exp}+N<h_{max}\\ K_{cam}(h_{max})&\text{ otherwise}\end{cases} \tag{8}\]  
where \(h_{ij}\) is the response of the camera's j-th receiver to the i-th emitter and \(K_{cam}\) is a function comprising the effects introduced by the camera's analog gain, the analog-to-digital conversion, and the nonlinear gamma correction. \(h_{max}\) is the full-well-capacity of the image sensor, measured in electrons. It must be noted that \(\mathbf{H}\) is subject to noise-induced errors, as the noise term \(N\) suggests (which presents the variance of Equation (5)). In addition, it has been assumed without loss of generality that the impulse response of the optical channel can be modeled as a Dirac's delta as the frequency response of the camera system is significantly smaller (bounded by \(T_{exp}\)) than the channel bandwidth of any OWC indoor scenario. Regarding the optical system's spatial response, it has been assumed that the light source presents uniform radiance.  
Furthermore, the second stage of the compensation is based on calculating the inverse of the obtained matrix and multiplying it by the received vector (Equation (9)). This vector implicitly includes the current CSI \(\mathbf{H}\), and this multiplication aims to mitigate the inter-channel interference. In an ideally compensated scenario, the interference term ofEquation (6) (right term of the denominator) would vanish, but the shot noise contribution of each channel would remain.  
\[v_{comp}=\overbrace{\mathbf{H}^{-1}\cdot\mathbf{\dot{H}}\cdot\mathbf{\dot{\theta }}_{tx}}^{\mathbf{I}_{H}}\cdot\mathbf{\dot{\theta}}_{tx} \tag{9}\]  
\(\mathbf{\dot{\theta}}_{tx}\) is the transmitted symbol, \(\mathbf{\dot{\theta}}_{rx}\) is the received symbol and \(v_{comp}\) is the CSI-compensated symbol. The performance of the channel compensation depends on several parameters, such as the condition number of \(\mathbf{H}\)[38] and the similarity between the latter and \(\mathbf{\dot{H}}\). If both matrices are identical, the compensation results in \(\mathbf{I}_{H}=\mathbf{I}\), where \(\mathbf{I}\) is the identity matrix. Static links (or under slow movement regimes) are generally assumed to work in a stationary condition. Nonetheless, as will be demonstrated in the following sections, this assumption does not hold anymore if temperature effects are included in the model.
# Analysis of the Vibration Characteristics of Ballastless Track on Bridges Using an Energy Method
## 3 Results and Discussion  
### Vertical Transfer Characteristics of Vibration Energy  
The running speed of the vehicle was set to 300 km/h, and the power flows of the track-bridge system and corresponding evaluation indices were obtained under this condition. The frequency-domain velocity and force results of the rail and fasteners are shown in Figure 5 as an example.  
Figure 6 reveals that there were many peaks of power flows in the frequency range investigated. In addition, the power flows decreased obviously from top to bottom of the track-bridge system. This is because the vibration gradually attenuates from top to bottom of the track-bridge system in the transfer process. The differences in power flows between the rail and the slab track reflect the attenuation effect of the fasteners. In addition, the differences in power flows between the slab track and the bridge reflect the attenuation effect of the slab mat layer as well. Furthermore, the attenuation effect of fasteners is more significant in the frequency range of 150 Hz to 1000 Hz, that is, at a high frequency range, whereas the attenuation effect of the slab mat layer is more obvious in the frequency range of 45 Hz to 200 Hz, that is, at a low frequency range. Therefore, it can be inferred that the fasteners have an obvious damping effect at a high frequency range; on the contrary, the slab mat layer presents an evident damping effect at a low frequency range.  
The PFTRs of the track-bridge system are shown in Figure 7. It can be observed that the PFTRs of the track-bridge system were all below 1.0, except for the power flows from the slab track to the bridge at some frequencies. In addition, the PFTRs from the rail to the slab track present a decreasing trend above 200 Hz, which is a result of the elasticity and damping effects of the fasteners. Nevertheless, the PFTRs from the slab track to the bridge show a downward trend in the frequency range of 10 Hz to 200 Hz, which is caused by the elasticity and damping effects of the slab mat layer. Therefore, conclusions can be drawn that the fasteners have a remarkable damping effect at a high frequency range, whereas the slab mat layer has a remarkable damping effect at a low frequency range.  
Figure 5: The frequency-domain (**a**) velocity and (**b**) force results.  
The AELVs of the track-bridge system are depicted in Figure 8. It can be observed that the AELVs of the rail, the slab, and the bridge present an order of large, medium, and small, respectively. This phenomenon arises from the elasticity and damping effects of fasteners and the slab mat layer. Moreover, the fasteners had a better vibration damping effect on the AELVs than the slab mat layer.  
Figure 8: The average energy levels of vibration (AELVs) of the track–bridge system.  
Figure 6: The power flows of the track–bridge system.  
Figure 7: The power flow transfer rates (PFFRs) from (**a**) the rail to the slab track and (**b**) the slab track to the bridge.
# Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service +
## 4 Anomalous Vehicle Behavior Detection Using mSSA
### SSA-Based Change Point Detection  
The original time series can be partitioned into multiple, different components of a time series. With a reasonable grouping strategy, components corresponding to noises or secondary factors can be removed, and only principal components are reserved. Basically, the operation of an SSA based change point detection algorithm is that it moves the sliding window and calculates the distance between the test matrix obtained from the target segmentation of the time series and the base matrix reconstructed from the \(l\)-dimensional subspace. An obvious distance between the test matrix and the base matrix will be observed when a change occurs.  
Considering a time series, assuming that \(N,M,l,r,s\) are fixed integers and \(n\) is the iteration step number. Here, \(N\) is the segmentation length and \(M\) is the window length, \(0<M<N/2\), \(0<r<s\). An \(l\) dimensional base matrix \(X^{(n)}_{base}\) is constructed by performing the steps one to three: embedding, performing SVD, and grouping. Denote \(U_{l}=[U_{1},\ldots,U_{l}]\) as the eigenvector subspace composed of the eigenvectors corresponding to first \(l\) eigenvalues. Similarly, the test matrix is constructed for the time series segmentation in \([n+r+1,n+s]\):  
\[X^{(n)}_{test}=[X^{(n)}_{n+r+1},\ldots,X^{(n)}_{n+s}] \tag{8}\]  
\[X^{(n)}_{j}=[X^{(n)}_{n+r+j^{\prime}},\ldots,X^{(n)}_{n+r+M+j-1}]^{T} \tag{9}\]  
where \(j=r+1,\ldots,s\). The size of the test matrix is \(M\times(s-r)\).  
Next, the Euclidean distance \(\mathcal{D}\) between the base matrix and the test matrix is calculated:  
\[\mathcal{D}_{n,l,r,s}=\sum_{j=r+1}^{s}((X^{(n)}_{i})^{T}X^{(n)}_{i}-(X^{(n)}_{ i})^{T}UU^{T}X^{(n)}_{i}) \tag{10}\]  
where vector \(X^{(n)}_{j}\) is each column of test matrix \(X^{(n)}_{test}\).
# A Review on Distribution System State Estimation Algorithms
## 3 DSSE Fundamentals and Main Algorithms
### State Vectors  
The state vector of the system is defined as a set of variables, which, unlike transmission systems, may be either Node-Voltage (NV)-based, i.e., NV-DSSE, or Branch-Current (BC)-based, i.e., BC-DSSE [13]. As there is no standard process, the selection of the appropriate state vector can be determined by the criteria presented in Table 1, which compares the two state vector options in terms of applicability, complexity, efficiency, and compatibility.  
NV-DSSE includes the voltages of each node and can be formed using either polar or rectangular coordinates. These state vectors are the most common in literature and also constitute the default state vectors of transmission systems [10,22]. Their main advantage, aside from their wide implementation and rich literature analysis, is that they can be used for all sorts of configurations [23], such as radial, mesh, etc. In addition, this sort of state vector is recommended for the easy incorporation of voltage measurements. However, it is noted that NV-DSSE is highly sensitive to measurement weights and is likely to face convergence issues [12]. Work related to NV-DSSE can be found in [17,24,25] using polar coordinates and in [23,26] using rectangular coordinates.  
BC-DSSE includes the currents of each branch and is usually formed using rectangular coordinates but can be also formed using polar coordinates [27,28]. For these state vectors, power measurements are expressed in terms of branch currents, and their use is recommended as a more straightforward process, especially when it comes to current measurements [9,11]. Even though BC-DSSE is mostly suitable for radial distribution networks, which is a major limitation, it should be mentioned that it is considered to have simple implementation, lower computational time, lower sensitivity (to measurement weights, etc.), and higher possibility to converge, compared to NV-DSSE [11,13]. Therefore, this approach is in many cases preferred to its counterpart. Research related to BC-DSSE is presented in [27,29] using rectangular coordinates and in [28,30] using polar coordinates.