esa-sceva/satcom-chunk-collection huggingface dataset:
# State Residualisation and Kron Reduction for Model Order Reduction of Energy Systems
## 5 Conclusions  
A combination of event- and non-projection state residualisation approximation and Kron reduction techniques has been presented, which maintains the advantages of both approaches. The former preserves the variables and physical structure of a system by using state residualisation instead of projections, retains some poles around the operating point (ensuring stability preservation), and adapts to the simulated event and observed variables. The latter simplifies the network representation of the obtained models, and thus further speeds up the time domain simulation. This method has been applied to a modified IEEE 39-bus system with 100% PE converters under different test cases. The results show that selecting the reduced model size to be the same as the phasor approximation reduces model complexity while achieving improved accuracy and faster simulation times. The main reason for the improvements seen is that the state residualisation method adapts to the test case under consideration and thus achieves the most appropriate reduced model, while the Kron reduction reduces the number of bus nodes required.  
Conceptualisation, methodology and resources, X.Z. and X.K.; software, validation, formal analysis, investigation, data curation and writing--original draft preparation, X.Z.; writing--review and editing, X.Z., X.K., Q.C., F.C. and D.F. All authors have read and agreed to the published version of the manuscript.  
The work has been supported by SEAI (Sustainable Energy Authority of Ireland) under RD&D Award 22/RDD/776. It has also been supported by a Ulysses award from the Irish Research Council between UCD and L2 EP, ENSAM.  
Not applicable.  
Data Availability Statement:Data is currently unavailable due to privacy restrictions.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Case** & **Full EMT Model** & **Phasor Model** & **MOR by State Residualisation and Kron Reduction** \\ \hline Case 1 & 100\% & 25.3\% & 21.6\% (order 53) \\ Case 2 & 100\% & 63.9\% & 54.1\% (order 101) \\ Case 3 & 100\% & 81.8\% & 65.4\% (order 112) \\ \hline \end{tabular}
\end{table}
Table 5: Simulation time of reduced models, using state residualisation and Kron reduction compared to phasor models (order 130), relative to full EMT models (order 308).
## 3 Examples and Discussion
### Structural Normalization and Direct Economic Loss
Under the action of a single earthquake event and mainshock-aftershocks, the direct economic loss of the normalized post-earthquake structure is shown in Equations (43) and (44):  
\[L_{\text{DE,L}}=5\%\times 1.0+20\%\times 0.8311+50\%\times 0.2803+97.5\%\times 0.0169=0.3728 \tag{43}\]  
\[L_{\text{DE,L}}^{\prime}=5\%\times 1.0+20\%\times 0.8790+50\%\times 0.3556+97.5 \%\times 0.0889=0.4903 \tag{44}\]  
Under the action of a single earthquake event and mainshock-aftershocks, the total normalized economic loss is  
\[L_{\text{DE}}=(1+\gamma)L_{DE,\text{L}}=1.7\times 0.0478=0.6338 \tag{45}\]  
\[L_{\text{DE}}^{\prime}=(1+\gamma)L_{\text{DE,L}}^{\prime}=1.7\times 0.0505=0.8335 \tag{46}\]  
The normalized economic loss of the structure after the earthquake, under the mainshock-aftershock, is slightly higher than the normalized economic loss under a single earthquake event. The difference \(\Delta L_{\text{DE}}\) between the two is calculated as follows:  
\[\Delta L_{\text{DE}}=L_{\text{DE}}-L_{\text{DE}}^{\prime}=0.1997 \tag{47}\]  
Table 3 shows that the loss index of the structure during the mainshock-aftershock is marginally higher than that during the mainshock alone; furthermore, as the intensity increases, the loss index exhibits a corresponding increase, and the difference of the loss index between the mainshock-aftershock and the main earthquake alone also expands.
# Wiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement
## 5 Experimental Setup  
To test the performance of the speech enhancement method proposed, the computation of speech quality measures on simulated speech samples was carried out.
# Machine Learning-Driven Dynamic Traffic Steering in 6G: A Novel Path Selection Scheme
## 5 Conclusions and Future Works  
Using user traffic data for unsupervised learning and supervised learning techniques facilitates the development of feasible classification functions to achieve the optimum traffic-transport assignment. A programmed network node with the classification model shall label the traffic based on the classification output that determines the selection of transport types for the traffic. The study eventually helps to automate the selection of a path for pertinent traffic, envisioning the 6G extensive TN and NTN aspirations for optimal \"service ubiquity\". Thus, the goal to enable highly efficient resource management could be realized to achieve higher performance requirements in 6G with low errors in the classification results that give more than a 90% classification accuracy score.  
The study also opens an avenue to impose a classification model based on an appropriate timeslot that could determine the frequency of the provisioning of inference models to be programmed to the pertinent nodes in an area. In addition, the entire study could lead to a new opportunity where each traffic requirement in an area could be handled by specific models that are dynamically created based on local demand (traffic diversity) and capacity (bandwidth resources and the heterogeneity of transport network provision in the area).  
Future studies should extend the variable from this study to determine the end-to-end flow of imposing models based on the timeslot into the programmed nodes using the O-RAN platform. The scope of this study requires the regressing steps on the models for respective timeslots to assess the performance of the classification model in a time-series format to establish the overall models' organization. The other variable is to learn the model's compatibility to produce and assign a highly efficient model for an area and other possible uses of the models in other areas. Lastly, there is potential for the incorporation of the organizational flow of this study in a federated learning framework. Finally, research on the workability and improvement could be harvested in the telecommunication infrastructure based on a federated learning approach.  
Conceptualization, H.A.H.N. and T.M.; methodology, H.A.H.N.; validation, H.A.H.N. and T.M.; formal analysis, H.A.H.N.; investigation, H.A.H.N.; resources, H.A.H.N.; data curation, H.A.H.N.; writing--original draft preparation, H.A.H.N.; writing--review and editing, H.A.H.N.; visualization, T.M.; supervision, T.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data of this research will be provided to interested individuals upon request to the corresponding author.  
The authors declare no conflicts of interest, and the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Techniques for Bessel and Perfect Vortex Beam Formation  
### Bessel Beams  
The electrical field of Bessel beams (BBs) is described by the expression  
\[E(\mathbf{r})=E_{0}J_{\ell}(\kappa r)\exp(ik_{z}z+i\ell\varphi), \tag{1}\]  
where \(J_{\ell}(\kappa r)\) is the Bessel function of the first kind of the order \(\ell\), \(k=\left(k_{z}^{2}+\kappa^{2}\right)^{1/2}\), and \(\varphi\) is the azimuth angle. Hence, they are (see, for instance, [2]) a superposition of plane waves:  
\[E(\mathbf{r})=E_{0}\exp(ik_{z}z)\int a_{\kappa\ell}(\mathbf{k}_{\perp})\,\exp (i\mathbf{k}\mathbf{r})\,\frac{\mathrm{d}^{2}k_{\perp}}{\left(2\pi\right)^{2}}, \tag{2}\]which converges conically to the optical axis under the angle  
\[\theta_{k}=\text{atan}(\kappa/k_{z}), \tag{3}\]  
and the Fourier amplitudes of which are described by the following expression:  
\[a_{\kappa\ell}(\mathbf{k}_{\perp})=i^{-\ell}\exp(i\ell\varphi_{k})\frac{2\pi}{k _{\perp}}\,\delta(k_{\perp}-\kappa). \tag{4}\]  
In optics, the quantum number \(\ell\) is called the topological charge. From the above expressions, one can see that the ideal Bessel wave has infinite transverse size, infinite energy, and, like a plane wave, cannot exist in reality. A superposition of Bessel waves, however, can be used for an analytic description of really existing radiation modes--for example, Laguerre-Gaussian beams.  
As seen from Equations (1)-(4), some properties of Bessel beams are of interest in terms of use in real-life optical systems. The maximum beam intensity is in the vicinity of the beam optical axis; the intensity in the beam cross-section does not change with distance; higher-order beams have an orbital angular momentum (OAM). Due to these properties, the beams are \"non-divergent\". The intensity distribution in the beam cross-section is capable of \"self-recovery\" after the beam's passage through obstacles and during the beam's propagation in turbulent media [20]. The beams can transfer angular momentum to micromechanical systems and particles [21; 22].
## 4 Time Domain Spectral Finite Element Method (TDSFEM)
### Wave Propagation in 3D Elements
Two examples, one for the validation of the proposed spectral finite element and the other for the demonstration of crack detection in plates, have been presented and discussed. The contact between the two faces of crack has been considered. The discussion of numerical says that only the anti-symmetric mode is prone to thickness locking, thus a remedy should be made only on this part. The proposed spectral finite element can adequately model the Lamb wave propagation in plate-like structures and the complex scattering for the crack. Finally, the crack location can be well determined by a PZT-induced Lamb wave-based diagnosis algorithm. A versatile character of the TDSFEM for modelling of different environments has been shown by [146], where the numerical simulation of acoustic waves in complex three-dimensional media has been proposed. This problems have been a subject of interest in many branches of science, from exploration geophysics to non-destructive testing and medical imaging. The article itself presents and validates a numerical technique based on an axisymmetric formulation of a TDSFEM in the time domain for heterogeneous fluid-solid media. Taking advantage of axisymmetry enables the study of relevant 3D configurations at a very moderate computational cost. The authors have presented and validated a numerical method based on time-domain spectral elements for coupled fluid solid full-wave propagation problems in an axisymmetric setting, including in cases with significant backscattering, which cannot be modelled, e.g., based on the parabolic equation approximation.
## 3 Materials and Methods
### Hybrid Sensing Platform for IoT-Based Precision Agriculture
#### 3.1.2 Aerial Node  
The HSP's aerial node is mounted on a quadcopter UAV. A quadcopter UAV is selected for the HSP because of its ability to hover and quickly change altitude while holding its position. The aerial node hosts five sensors. These include the AMG8833 grid-eye IR and AS7265x spectrometer sensors that are similar to the corresponding sensors deployed in the ground-based nodes, enabling direct data comparison between the aerial and ground-based  
Figure 2: Plant observation component design.  
data observations from these sensors. In addition, the aerial node includes an AM2315 temperature and humidity sensor, an ultrasonic distance sensor to measure the UAV distance from the ground, and an RGB camera to record aerial images for image processing purposes. Figure 3 shows the quadcopter UAV and the sensors deployed underneath the UAV.  
The aerial data are collected from the sensors using the Arduino Mega microcontroller and recorded on an SD card. The data are then transferred to the Raspberry Pi using serial communication via a Universal Serial Bus (USB) standard. An LTE module is used to provide an Internet connection for the Raspberry Pi and transfer the data to the IoT platform. Figure 4 shows the aerial node design.
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 3 Methodology
### 2 Dpca  
In contrast to traditional PCA, 2 DPCA [13] depends on 2D image matrices rather than 1D image vectors. Therefore, the image matrices do not need to be reshaped into vectors prior. Instead, an image covariance matrix can be constructed based on the image matrices in a direct manner. To be specific, 2 DPCA [13] aims at finding a projection matrix \(\mathbf{U}\in\mathbb{R}^{w\times k}\)  
\[\max_{\mathbf{U}^{T}\mathbf{U}=\mathbf{I}_{k}}tr\mathbf{U}^{T}\frac{1}{n-1} \sum_{i=1}^{n}(\mathbf{X}_{i}-\overline{\mathbf{X}})^{T}(\mathbf{X}_{i}- \overline{\mathbf{X}})\mathbf{U}, \tag{6}\]  
where \(k\) is the feature number and \(\overline{\mathbf{X}}=\frac{1}{n}\sum_{i=1}^{n}\mathbf{X}_{i}\) denotes the mean image. The projection matrix \(\mathbf{U}\) is calculated by eigen-decomposition of the image covariance matrix  
\[\frac{1}{n-1}\sum_{i=1}^{n}(\mathbf{X}_{i}-\overline{\mathbf{X}})^{T}(\mathbf{ X}_{i}-\overline{\mathbf{X}})\in\mathbb{R}^{w\times w} \tag{7}\]and then selecting the eigenvectors corresponding to the \(k\) largest eigenvalues [13]. The percentage of total variance explained by these eigenvectors can be calculated similar to Equation (3). For a test image sample \(\mathbf{\widetilde{X}}\in\mathbb{R}^{h\times w}\), its feature matrix is  
\[(\mathbf{\widetilde{X}}-\mathbf{\widetilde{X}})\mathbf{U}\in\mathbb{R}^{h\times k}. \tag{8}\]  
The reconstruction image of sample \(\mathbf{\widetilde{X}}\) is  
\[(\mathbf{\widetilde{X}}-\mathbf{\widetilde{X}})\mathbf{U}\mathbf{U}^{T}+ \mathbf{\widetilde{X}}\in\mathbb{R}^{h\times w}. \tag{9}\]  
The original 2 DPCA only projects the rows of images onto the projection matrix, as shown in Equations (6) and (8). Therefore, 2 DPCA lacks symmetry in the sense that it treats the rows and columns of images unequally.
# Machine-Learning-Based Path Loss Prediction
## 2 Theory
### Machine-Learning-Based Predictive Modeling
#### 2.2.5 Support Vector Regression (SVR)  
Support Vector Regression (SVR) is a machine learning method based on statistical learning theory [20]. The main goal of SVR is to find a hyperplane in high-dimensional space with the following formula:  
\[f(x)=w^{T}\phi(x)+b \tag{10}\]  
where \(w\) shows the normal vector of the hyperplane, \(x\) represents the input feature vector, \(\phi(\cdot)\) depicts the nonlinear transformation function, and \(b\) is the bias term. The approximate function of SVR is expressed by solving the dual problem as follows:  
\[f(x)=\sum_{i=1}^{N}(\alpha_{i}-\alpha_{i}^{*})K(x_{i},x)+b \tag{11}\]  
where, \(\alpha_{i}\) and \(\alpha_{i}^{*}\) are Lagrange multipliers and \(K(\cdot,\cdot)\) is the kernel function. The commonly used Gaussian kernel function is given by  
\[K(x_{i},x_{j})=\exp\Bigl{(}-\gamma\|x_{i}-x_{j}\|^{2}\Bigr{)} \tag{12}\]  
where \(x_{i}\) and \(x_{j}\) represent the feature vectors of the support vectors used in the SVR model.
## 5 Discussion and Conclusions
### Platforms, Methods and Results  
The systematic review shows the relevance of concepts such as remote sensing data and machine learning approaches in the contexts of the digital transition and food security worldwide. Unmanned aerial vehicle platforms, satellite information, the Copernicus program, shuttle radar topographic mission, moderate resolution imaging spectroradiometer and Google Earth Engine are platforms/ methodologies/designations considered to collect/gather/analyze big data. Gaussian process regression, support vector machine regression, random forest regression, neural network, deep neural networks, 1D convolutional neural networks, long short-term memory networks, ridge regression, light gradient boosting, Bayesian neural network and adaptive boosting are some of the approaches/designations highlighted by the literature for the machine learning assessments. This literature review highlights the relevance of the Google Earth Engine platform and the Random Forest in the interrelationships between the machine learning and food security topics. These approaches are considered to predict crop mapping and yield with high precision, in some cases with R squares of about 99% and root mean squared error around 1%. These technologies are in some contexts in the beginning of being implemented in the agricultural and food frameworks, but there is an enormous field to be explored [84, 85] and this research may be a relevant contribution to show gaps, trends and opportunities for the several stakeholders.
# Signal Design and Compatibility Assessment for LEO Navigation Augmentation System +
## 2 Signal Design Requirements
### High Data Rate Requirement  
The broadcast ephemeris cannot support to perform a PPP solution, and high-precision GNSS precise ephemeris and clock messages need to be broadcasted. The broadcasting period of precise ephemeris is usually 30 s, and precise clock messages are 5 s, such as the CLAS service of QZSS. As the number of the supported GNSS satellites increases, the bit number of precision messages also increases linearly, leading to an increase in the required data rate.  
For the LEO navigation augmentation system, it is not only necessary to broadcast GNSS precise messages but also to broadcast its own precise ephemeris and clock messages. In addition, the almanac data of LEO satellites also increases with the increase in the number of LEO satellites. These factors all require LEO navigation signals to support high data rates of about 2 kbps.
# Design of Tunable Broadband Graphene-Based Metasurface with Amplitude-Phase Modulation
## 2 Theoretical Analysis
### Graphene Conductivity
Eventually, the surface conductivity of graphene is manipulated. The adjustable Fermi level of graphene provides an idea for dynamic modulation of electromagnetic beams using graphene-based metasurfaces.
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 4 Experimental Results
### Embedding Capacity
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \((\alpha,\beta)\) & \((1,2)\) & \((2,2)\) & \((3,2)\) & \((4,2)\) & \((5,2)\) & \((6,2)\) & \((7,2)\) \\ \hline Lena & / & 0.3912 & 1.6654 & 2.6812 & 2.6413 & 1.9245 & 0.9945 \\ Man & / & / & 0.8173 & 2.0054 & 2.4745 & 1.9015 & 0.9831 \\ Jetplane & / & 1.5354 & 2.6098 & 3.0233 & 2.6745 & 1.9245 & 0.9945 \\ Baboon & / & / & / & 0.2015 & 0.9613 & 1.2445 & 0.8615 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The embedding rate of the IPBTL-RDHEI scheme when \(\beta=2\) and \(\alpha=2\sim 7\). In Tables 5-8, \"/\" indicates that it is not possible to embed information in this case. The bold data are the best embedding rate for the images. The embedding rates of these four images in the proposed EPBTL scheme can reach 3.1249, 2.8687, 3.3413, and 1.5125, respectively, which have an increase of about 0.3 bpp compared with the results of the IPBTL-RDHEI scheme, i.e., 2.7872, 2.5517, 3.0589, and 1.2502, respectively. The increase of data payload is due to the reduction of the size of auxiliary information. The reduction in auxiliary information is due to the fact that the remaining \(8-\beta\) bits of the pixel point in the \(P_{ns}\) group are used to record the prediction error for that pixel point. The prediction error can recover the value of that pixel so that the auxiliary information does not need to record the original pixel value of that pixel. Because of the high number of pixels belonging to the \(P_{ns}\) group, there is a significant embedding rate improvement in the proposed EPBTL scheme.
## 4 Experiments
### Datasets  
We selected transcript datasets from Refseq ([[ftp://ftp.ncbi.nih.gov/refseq/](ftp://ftp.ncbi.nih.gov/refseq/)]([ftp://ftp.ncbi.nih.gov/refseq/](ftp://ftp.ncbi.nih.gov/refseq/)) (accessed on 2 January 2025)), which provides a complete well-annotated collection of biological molecules for various types of species. We downloaded transcriptome-wide mRNA sequences with prefixes 'NM_', and a total number of 24,842 human and 19,900 mouse transcripts are obtained after clean up procedure. All these transcripts have canonical TISs. After redundancy removal by using CD-hit [36] with sequence identity cutoff 80%, there are 20,488 and 11,613 sequences left for human and mouse transcripts, respectively, from which we adopted the hold-out strategy and randomly selected 4/5 and 3/4 sequences as training set and 1/5 and 1/4 sequences as test set for human and mouse datasets, respectively.  
To further validate the effectiveness of our method in discovering novel TISs, we leveraged a curated _A. thaliana_ mRNA dataset, which comprises 6974 experimentally validated TISs via ribosome profiling alongside 258,553 high-confidence false TISs, providing a test benchmark for assessing cross-species predictive performance under biologically realistic conditions.
## 3 Numerical Modeling and Validation  
The numerical model in this study was constructed in COMSOL Multiphysics 6.2 finite element software based on the Hoek-Brown criterion. Post-establishment validation was performed via comparative analysis with the V-D theoretical curve defined in Equations (7) and (8).
## 3 Fabrication Results and Measurement  
This study fabricated, assembled, and measured a DR, which was split into optimized pixels. The dielectric material was 99% alumina ceramic with a dielectric constant of 9.6 and a loss tangent of 0.0001. Each pixel was divided into an easy form during the milling process and processed into a total of 53 parts. The DR parts were manufactured by attaching adhesives and were attached to the ground plane. The manufactured antenna shape is shown in Figure 7. The under \(-10\) dB reflection bandwidth of the antenna, which was measured using a network analyzer, was 2.96-3.44 GHz (14.7%), and the simulated bandwidth was 3.06-3.46 GHz (12.3%). At an operating bandwidth with wide HPBW (3.25-3.35 GHz), simulated and measured boresight gain of the antenna was 2.41\(\sim\)\(-\)0.81 dBi and 1.84\(\sim\)\(-\)0.19 dBi, respectively. The resulting graphs in Figure 8a,b reveal a good correspondence between the measurements and the simulation data, and the error is due to cable loss and manufacturing tolerance generated during fabrication.  
Figure 9 shows the measured and simulated radiation patterns. The two results reveal a good match. Table 3 presents the 3-dB beamwidth according to the frequency in the operation band.  
Table 4 shows the comparison of the proposed antenna with the recent studies of wide HPBW DRA. The proposed antenna has a wider HPBW in the E and H-planes than [5] and a narrower E-plane than [3,4] but a wider H-plane HPBW. In addition, although the height of the antenna is higher than those of [3, 4, 5], the antenna area occupied is much smaller.  
Figure 8: Measurement results of the fabricated antenna and the simulation. (**a**) The reflection coefficient and (**b**) the boresight gain.  
Figure 7: A photograph of the fabricated antenna and DR parts.