esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results and Evaluations
### Classification Result Comparison with Benchmark Models  
We compared our experimental classification results with those for some baseline models. All the researches have a unique goal to achieve by respective research methodology. Nevertheless, we compared our proposed research with the most similar works. The results are presented in Table 9.
## 2 Methods
### Operational Modal Analysis  
In the process of OMA, the expression of response transmissibility in the multiple-degrees-of-freedom system is as follows [35]:  
\[T_{io}(\omega)=\frac{X_{i}(\omega)}{X_{o}(\omega)} \tag{7}\]  
where \(T_{io}(\omega)\) is response transmissibility; \(X_{i}(\omega)\) and \(X_{o}(\omega)\) are the Fourier transforms of \(x_{i}(t)\) and \(x_{o}(t)\) at the Degrees of Freedom (DOF) \(i\) and \(o\), respectively.  
The expression of Power Spectrum Density (PSD) transmissibility is as follows:  
\[\hat{T}_{io}(\omega)=\frac{S_{i}(\omega)}{S_{o}(\omega)}=\frac{X_{i}(\omega)X _{i}^{*}(\omega)}{X_{o}(\omega)X_{o}^{*}(\omega)} \tag{8}\]  
where \(X_{o}^{*}(\omega)\) is the conjugate complex number of \(X_{o}(\omega)\), \(S_{i,o}(\omega)\) is the Cross Power Spectrum Density (CPSD) of \(x_{i}(\omega)\) and \(x_{o}(\omega)\); \(S_{o,o}(\omega)\) is the PSD of \(x_{o}(\omega)\). The PSD transmissibility can be expressed as [36]:  
\[\hat{T}_{io}(\omega)=\frac{X_{i}(\omega)}{X_{o}(\omega)}=\frac{H_{ip}(\omega) \cdot F_{p}(\omega)}{H_{op}(\omega)\cdot F_{p}(\omega)}=\frac{H_{ip}(\omega)} {H_{op}(\omega)}=T_{io}(\omega) \tag{9}\]  
where \(H_{ip}(\omega)\) is the Frequency Response Function (FRF) between the degrees of freedom \(i\) and \(p\)[37], \(F_{p}(\omega)\) is the Fourier transform of excitation force signal at point \(P\). From the Equation (7), we can get the following:  
\[\lim_{\omega\rightarrow\omega_{r}}\hat{T}_{io}(\omega)=\lim_{\omega\rightarrow \omega_{r}}\frac{H_{ip}(\omega)}{H_{op}(\omega)}=\frac{\sigma_{i}}{\varphi_{no}} \tag{10}\]  
Take the node of degree of freedom \(o\  
The structural modal shape can be obtained from Equation (7):  
\[\varphi_{r}=\underset{\omega\rightarrow\omega_{r}}{lim}\Big{\{}\widehat{T}_{1o}( \omega),\widehat{T}_{2o}(\omega),\dots,\widehat{T}_{no}(\omega)\Big{\}} \tag{12}\]  
Substituting Equation (7) into Equation (11):  
\[\varphi_{r}=\underset{\omega\rightarrow\omega_{r}}{lim}\{S_{1,o}(\omega),S_{2,o }(\omega),\dots,S_{n,\rho}(\omega)\} \tag{13}\]  
The modal parameter identification process of a structure by OMA is shown in Figure 2. The PSD curve can be obtained from vibration signal under the reference DOF, and the structural natural frequency is obtained from the PSD curve [38]. The CPSD can be obtained from vibration signal under the reference DOF and other DOF, and the PSD transmissibility can be obtained according to Equation (8). The mode shape can be obtained by the PSD transmissibility.
# Detecting Jamming in Smart Grid Communications via Deep Learning
## 6 Performance Evaluation
### Relative Jamming Power (RJP)  
We investigate the impact of the jamming power by fixing the distance between \(\mathcal{E}\)and \(\mathcal{B}\) at 65 m and varying the power of the signal injected by the jammer, i.e., the RJP. As previously conducted, we evaluate the performance of our solution in the settings of _Scenario 1_ using the networks _resnet18, resnet50_, and _inceptionv3_, while we use the autoencoder for _Scenario 2_.  
_Scenario 1._ Figure 16 shows the accuracy of the CNN as a function of the RJP. We observe that an RJP equal to 0.1 is enough to guarantee an accuracy of about 1. This result should be compared with Figure 14, where RJP was set to 1. In fact, we highlight that the jammer can be detected with overwhelming probability even when transmitting at a negligible fraction of the power, i.e., RJP = 1.  
Figure 14: \(\mathcal{E}\)-\(\mathcal{B}\) distance analysis. Performance evaluation of Scenario 1 (CNN) and Scenario 2 (autoencoder) assuming \(RJP=1\), the distance of \(\mathcal{A}\)-\(\mathcal{B}\) being equal to 30 m while varying the distance of \(\mathcal{E}\)-\(\mathcal{B}\). (**a,b**) Refer to Scenario 1 (CNN), while (**c,d**) refer to Scenario 2 (autoencoder).  
_Scenario 2._ Finally, we consider the performance of the autoencoder by varying the RJP. Figure 16b shows the accuracy, precision, recall, and F1-score as a function of the RJP while setting the \(\mathcal{E}\)-\(\mathcal{B}\) distance to 65 m. We observe that the autoencoder requires the jammer to use a higher RJP (0.5) compared to the previous case (Figure 16a) to work effectively for anomaly detection. As discussed for the distance analysis, CNNs are trained on jammed signals, making them more sensitive and capable of detecting the presence of the jammer, even in the presence of lower RJP.
## 4 Results and Analysis
### Training and Testing Subset Configuration  
For analyzing the fault detection and fault identification capabilities of the proposed methodology, the modeled dataset consisting of 2,000,000 samples (500,000 data instances per class) was randomly split into training and testing subsets. Specifically, 1,500,000 samples (375,000 instances per class) were used for training SVM classification algorithms, whereas the remaining previously unobserved 500,000 samples (125,000 instances per class) were utilized for the testing process. The similar dataset perturbations were applied to the residual signals obtained by the referenced NHVSO and VSO methods in order to perform the comparison between them and the proposed technique used in conjunction with the SVM classification algorithm.
## 3 Algorithms
### Contour Parallel Algorithms
#### 3.2.2 Geodesic Contour Parallel Spirals (Geo-Contour)  
In some cases, the gaps created by missing contours in the local maxima are of concern as they are suspected to degrade material quality by introducing points where fractures or cracks form and decrease the fiber area fraction. To mitigate this, the global continuity is sacrificed in favor of spirals that travel into the maxima, filling them with a final path segment. This would not be achievable with the with Geo-FISO as two segments, created by splitting a closed curve, are needed to retain the inward-outward movement necessary for the algorithm. The approach described here is displayed in Figure 4b.  
The inputs and first steps are identical to those in Geo-FISO. Upon obtaining the minimum spanning tree, all nodes \(v_{i,j}\) with \(degree(v_{i,j})>2\) are denoted as rerouting contours and removed from the tree. The remaining connected components represent the \"spiralable\" areas. These are the areas around local maxima in \(\phi\), which can be attributed as belonging to this local maximum. The corresponding curves are then rerouted to obtain a continuous path. For each of these spirals, the points that lie at the local maximum are computed with special caution for numerical errors and an additional curve is appended to the path which traverses these points. After obtaining all spirals, they are connected by travel motions as before and the rerouting contours are traversed.
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 4 Current Research Trends, Challenges, Solutions, and Future Research Directions
### Challenges, Solutions, and Future Research Directions
#### 4.2.2. Possible Solutions  
* **Advanced Algorithms:** Developing sophisticated algorithms for beam selection, tracking, and adaptation can mitigate alignment problems and optimize system performance [20].
* **AI and Machine Learning:** Machine learning techniques can be employed to predict channel conditions, optimize beamforming in real time, and manage interference effectively [21].  
Figure 13. Challenges, solutions, and future research directions in Mw-wave hybrid beamforming field.  
* **Energy-Efficient Designs:** Hardware and circuit-level innovations that focus on energy efficiency can help reduce power consumption.
* **Scalable Architectures:** Modular beamforming designs that can scale up or down, depending on system requirements, may reduce complexity and cost [124].
* **Enhanced Channel Estimation Methods:** Low-complexity channel estimation methods and protocols for efficient feedback can improve overall system performance [136].
* **Smart Reflecting Surfaces:** Intelligent surfaces with reflective elements can be used to dynamically modify the propagation environment to improve signal coverage and reduce interference.
# Ka-Band Doppler Scatterometry: A Strong Wind Case Study
## 4 Sea Surface Doppler Centroid
### Small Incidence Angle (Record #5)
Figure 12: 2-D frequency-elevation spectra (**a,b**) measured by triangle wave gauge array and (**c,d**) simulated by merging measured spectra (\(f<0.3\) Hz) and JONSWAP model spectra [82] (\(f>0.3\) Hz). Horizontal dashed lines show instantaneous wind direction. observed wave spectra (rather than wind-based parameterizations) are key for accurate WIDV simulations, especially in complex wave conditions.
## 4 Calculation of the Load Rise Space
### Calculation of the Load Rise Space  
By considering the space for load rise due to load fluctuations in the allocation of section margins, it is possible to avoid a decrease in the section's transmission power due to untimely dispatching when the section increases in load.  
The typical operation mode of the REG clusters obtained from the cluster analysis is selected, and the load rise space for each typical operation mode of the REG clusters, \(P^{Lf}_{j,t}\), is found.  
\[P^{Lf}_{j,t}=\frac{\sum\limits_{k=1}^{K}\left(P_{t,j,k}-P_{t,j,ave}\right)}{K},j=1,2,\cdots J \tag{11}\]  
where \(j\) is the typical operation mode of each cluster obtained after clustering and analyzing the operation mode of the REG cluster, and \(J\) is the total number of typical operation modes.  
\(P_{t,j,ave}\) is the load average value of the system under the operation mode \(j\). \(P_{t,j,k}\) is the value of the load in the system greater than \(P_{t,j,ave}\) under the operation mode \(j\). \(K\) is the total number of occurrences of \(P_{t,j,k}\).  
From Equation (11), it can be seen that the larger the value of the load rise space \(P_{j,t}^{Lf}\) within the REG cluster, the greater the number and magnitude of loads above the load mean in the operation mode \(j\), resulting in a greater likelihood that the delivery section of the REG cluster will not be able to reach the upper limit of the section's deliverable power.  
In the process of section margin allocation, the current operation mode is matched with the operation mode database to determine what typical operation mode the current operation mode belongs to. Based on the load rise space of each typical operation mode of the REG cluster calculated by Equation (11), the load rise space under the current operation mode is obtained.  
In the event that the REG cluster experiences an increase in load, resulting in an underutilized section, and the dispatch center has not carried out a new round of section margin allocation, the dispatch center should quickly issue dispatch instructions to each REG station within the cluster so that the power delivered by the REG cluster to the grid still maintains the section power transmission limit.
## 3 Results and Discussion
### Morphological and Structural Characterization
In addition, the Si/O atomic ratio is more than 1/2 compared to that of the stoichiometric SiO\({}_{2}\), which indicates that more oxygen vacancies are exposed on the surface of the coal gangue after the acid-alkali activation treatment. The high-resolution Si 2p spectra are displayed in Figure 4b. Both the spectra of the activated coal gangue and the crushed coal gangue had to be fitted into two peaks located at 104.2 and 103.6 eV, which represent the existence of the strong Si-C and Si-O bonds, respectively [28]. Combined with the previous XRD data, it is shown that the surface of the activated coal gangue in situ forms SiO\({}_{\text{X}}\) and SiC\({}_{\text{X}}\) with stable amorphous structures, which has been proven to be helpful in improving the electrocatalytic activity of coal gangue [29]. To make more efforts in verifying the conjecture ahead, the C 1s and O 1s spectra of the samples were tested and analyzed in-depth. As shown in Figure 4c, the C 1s spectra could be deconvoluted into three characteristic peaks centralized at 289.5, 286.3 and 284.8 eV for both the activated coal gangue and crushed coal gangue, which related to the existence of \(\pi\)-\(\pi^{\star}\), C-O, and C-C, respectively. In contrast, the intensities of \(\pi\)-\(\pi^{\star}\) and C-O are both raised in the activated coal gangue, suggesting that there are abundant carbon-based functional groups and strong chemical bonds between carbon and oxide formed on the activated coal gangue. The O 1s spectra in Figure 4d could be well fitted into two peaks at 530.7 and 533.4 eV, corresponding to the lattice oxygen and defected oxygen [30]. Evidently, the intensity of defected oxygen peak is significantly enhanced over lattice oxygen for activated coal gangue. The result suggested that abundant oxygen vacancies are created  
Figure 3: XRD patterns of the as-prepared samples. in non-stoichiometric SiO\({}_{\rm X}\), which could further improve the electrochemical activity of coal gangue [31]. As shown in Table S1 (Supporting Information), the Si/C and Si/O ratio in activated coal gangue is higher than those in crushed coal gangue, suggesting the acid-alkali activation treatment could remove the impurities of gangue and retain silica-based compounds. Besides, the Si/O atomic ratio is more than 1/2 by compared by that of stoichiometric SiO2, which indicating that more oxygen vacancies is exposed on the surface of coal gangue after acid-alkali activation treatment. On the basis of the SEM, XRD, and XPS analyses, it could be concluded that the amorphous hybrid SiC\({}_{\rm X}\)/SiO\({}_{\rm X}\) with rich oxygen vacancies is successfully formed in situ on the surface of coal gangue after acid-alkali activation treatment.
## 5 Conclusions  
Both AnTT and PT3 were found to prevent MIA-induced decreases in the viability of chondrocytes, most likely through their ability to reduce oxidative stress. AnTT possesses additional properties of promoting the self-repair response of chondrocytes exposed to MIA, producing a higher COL2\(\alpha\)1/COL1\(\alpha\) ratio and higher COL2\(\alpha\)1, SOX9 and aggrecan levels. AnTT appears to be more effective than PT3 in protecting chondrocytes based on the dose and mechanism of action. Doctorionol could be developed as a supplement to reduce chondrocyte damage in OA, pending further validation from more comprehensive research.  
Conceptualization, N.A.G., I.N.S. and K.-Y.C.; methodology, K.-L.P.; validation, N.A.G., I.N.S. and K.-Y.C.; formal analysis, K.-L.P. and K.-Y.C.; data curation, K.-L.P.; writing--original draft preparation, K.-L.P.; writing--review and editing, N.A.G., I.N.S. and K.-Y.C.; visualization, K.-L.P. and K.-Y.C.; supervision, K.-Y.C.; funding acquisition, K.-Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Education, Malaysia, grant number FRGS/1/2018/SKK10/UKM/03/1. [PERSON] is a postdoctoral researcher funded by Universiti Kebangsaan Malaysia through Postdoctoral Research Scheme (RGA1).  
Not applicable.  
The authors acknowledge the Department of Anatomy and the Department of Biochemistry, Faculty of Medicine, Universiti Kebangsaan Malaysia for providing the Multiskan GO microplate reader and flow cytometry facilities.  
The authors declare no conflict of interest.  
Figure 6: Proposed mechanism of action of tocotrienols on chondrocytes. Both AnTT and PT3 suppress oxidative stress induced by MIA. AnTT also enhances the self-repair mechanism and anabolic activities of chondrocytes exposed to MIA. Abbreviations: \(\uparrow\), cause or induce; \(\uparrow\), inhibit; AnTT, anantatto tocotrienols; COL2\(\alpha\)1, collagen type II \(\alpha\)1; PT3, palm tocotrienol-rich fraction; SOX9, sex-determining region Y box protein 9.
## 3 Method
### Non-Gradient Methods  
The non-gradient method could be applied if either the direction of function descent is calculated without taking a gradient, like in [41] the simplex or Powell methods [40; 42], or if the number of points to be checked for being the extremum is relatively small and we can examine them one by one. We will follow the second approach for the problem of mapping two 3D objects.  
If we look at spherical harmonics inside correspondent degrees (see Figure 2), we can notice that when the band has a low number, the spherical harmonics have a relatively limited number of local extrema (the local peaks on the 3D surface of the harmonic). When we find a weighted sum of these harmonics, the number of local extrema at the resulting surface will also be limited and relatively small.  
Considering that function \(f\) is a weighted sum of spherical harmonics, and the spherical harmonics of different degrees are independent, we can say that function \(f\) itself is the sum of partial reconstructions corresponding to every degree. These partial reconstructions for degree \(l\) for 3D objects \(O_{1}\) and \(O_{2}\) will also be rotated at the same angle as the original objects, \(O_{1}\) and \(O_{2}\). This observation is accurate for any specific degree or combination of degrees. Also, it is accurate for whatever square-integrable function on the surface of a sphere is analyzed. This means that exactly the same angle of rotation will be calculated (up to the error of calculation) for every partial reconstruction of degree \(l\) on every concentric sphere.  
Figure 2: Spherical harmonics (degrees from 0 to 4). The green areas correspond to the negative values of the harmonics, and the blue ones correspond to the positive ones.  
Having a moderate number of local extrema at the partial reconstruction for level \(l\) for \(O_{1}\) and \(O_{2}\) and considering that all the rotations are around the center of coordinates, the angle of rotation to rotationally match the peaks of reconstructed surfaces to each other could be performed, for example, by an exhaustive search of all possible configurations in a low-dimensional space. This calculation provides a rotation matrix to match \(O_{1}\) and \(O_{2}\). Instead of the exhaustive search (due to a limited number of top-valued extrema generated), the solution could be found by analyses of only several possible combinations. The measure of the accuracy of mapping could be, in addition to \(M_{1}\) (Formula (7)) and \(M_{2}\) (Formula (8)), the Root-Mean-Square Deviation between two reconstructions.
# Multilevel Coding for the Full-Duplex Decode-Compress-Forward Relay Channel
## 2 Preliminaries  
### The Relay Channel  
The capacity of the full-duplex relay channel is in general unknown. The capacity of the degraded relay channel is achieved by decode-forward, and achievable rates for several other protocols have also been calculated including partial decode-forward and compress-forward. In a full-duplex relay, these protocols are implemented via block-Markov encoding [26], a consequence of the causality constraint on the relay.  
Throughout the paper, we denote the signal transmitted from the source node and the relay node in block \(t\) by \(X_{1}^{(t)}\) and \(X_{2}^{(t)}\). We begin with the received signal model at the relay, which experiences self-interference:  
\[Y_{2}^{(t)}=H_{12}X_{1}^{(t)}+n_{2}+n_{s},\]  
where \(H_{12}\) is the channel coefficient from the source to the relay, \(n_{2}\) is the additive Gaussian (thermal) noise at the receiver, and \(n_{s}\) is the sampled residual self-interference. The area of modeling and analyzing loop-back or self-interference has experienced rapid growth in the past few years. Several methods for mitigating self-interference are now in place, among them antenna design and placement (including passive components), as well as echo cancellation in the amplifier stage, as well as digital signal processing after down-conversion and sampling [27]. The collection of these methods has allowed the residual self-interference to be reduced significantly. The _residue_ of self-interference, \(n_{s}\), is the component that is seen by the relay decoder. Several works to date [27; 28; 29] have used a Gaussian model for \(n_{s}\), an approximation that is confirmed by various measurements [30; 31]. Therefore, the combination \(\tilde{n}_{2}=n_{2}+n_{s}\) is also Gaussian with the appropriate variance.  
Thus, the received signal at the relay and destination in block \(t\) are respectively given by  
\[Y_{2}^{(t)} =H_{12}X_{1}^{(t)}+\tilde{n}_{2}, \tag{1}\] \[Y_{3}^{(t)} =H_{13}X_{1}^{(t)}+H_{23}X_{2}^{(t)}+n_{3}, \tag{2}\]  
where \(H_{12},H_{13}\) and \(H_{23}\) are the channel coefficients as illustrated in Figure 1. The channel coefficients can include a path-loss component as well as any type of fading.  
The destination uses either backward decoding or a sliding window decoding. The former requires waiting until a terminating point where no new message is transmitted from the source, and, in the latter, the decoder uses a window of \(L\) blocks for decoding.  
In decode-forward, the relay decodes the message from the source in block \(t\) and assists the source in its transmission during block \(t+1\). In compress-forward, the relay does not attempt decoding but quantizes the received signal \(Y_{2}\) and sends the quantized version to the destination.  
Figure 1: Full-Duplex relay channel.
# Augmented Reality-Assisted Surgical Exposure of an Impacted Tooth: A Pilot Study
## 5 Conclusions  
Using AR software, it was possible to visualise the endosseous position of the crown of an impacted tooth to plan the surgical exposure in a precise and minimally invasive manner.  
Automatic detection of the 3D file to enable AR proved satisfactory for realising the clinical case. The clinical advantage is proportional to the time dedicated to creating files to upload to the software. AR using anatomical references reduces the operation time and costs compared to other surgically-assisted techniques that require surgical or extraoral templates.  
AR in oral surgery has enormous potential to support the surgeon in identifying anatomical information collected by the CBCT concerning the structures involved in the intervention. However, new studies are needed to assess this digital workflow's accuracy and to quantify AR precision and accuracy.  
The improvement of software utilising algorithms for anatomical recognition and tracking will play a pivotal role in advancing the use of Augmented Reality in oral surgery. Enhancing the accuracy, precision and real-time capabilities of these software algorithms can significantly enhance surgical guidance and personalized treatment planning in the surgical exposure of impacted teeth.  
Conceptualization, V.D., S.T., M.M. and F.F.; methodology, M.M., V.D. and F.F.; validation M.M. and F.F.; formal analysis, G.D., S.T., M.M., V.D. and F.F.; investigation, M.M. and F.F.; resources, M.M. and F.F.; data curation, M.M., G.D. and F.F.; writing--original draft preparation, M.M., G.D. and S.T.; writing--review and editing, M.M.; visualisation, M.M.; supervision, M.M.; project administration, M.M.; funding acquisition F.F. All authors have read and agreed to the published version of the manuscript.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the U.S. National Library of Medicine (protocol code NCT05536765).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare that the research was conducted without commercial or financial relationships construed as a potential conflict of interest.