esa-sceva/satcom-chunk-collection huggingface dataset:
# Developing an Intelligent Cellular Structure Design for a UAV Wireless Communication Topology
## 3 Proposed UAV Topology and Simulation
### Cellular Structure Design  
This subsection gives an outline of the cellular structure design from a UAV perspective, as can be seen in Figure 4, while Figure 5 illustrates a coordinate system of a UAV's cellular structure. The intelligent heterogenous 5G topology uses a smart MIMO antenna, which has several benefits, such as increased link and data reliability, simplifying the multiple access layer, bridging coverage gaps between cells or cells overlap, offering energy-saving resources, utilizing self-organized frequency reuse, and enhancing cellular cell size and location. Therefore, the proposed AI has a place in optimization and adaptability based on real scenarios of a designated area, which is the main contribution of this paper. To note, the smart MIMO antenna that is on-board a UAV uses a beamforming technique. As shown in Figures 4 and 5, there are 7 parameters that have been identified to design and optimize a cellular cell [46]. These parameters are, also, considered as inputs for the proposed AI. The parameters are:  
1. Central cell radius
2. Beamwidth
3. Beams angles
4. Tiers number
5. Environment type (Urban, Suburban, Rural)
6. Population density
7. Probability of Building DistributionEquations (1)-(6) represent the cellular structure design using beamforming and a smart MIMO antenna.  
\[b=h\ (\tan\biggl{(}\theta o+\frac{BW\theta}{2}\biggr{)}-\tan\biggl{(}\theta o- \frac{BW\theta}{2}\biggr{)}) \tag{1}\]  
\[a=2h\ \sec\ (\theta c)\tan\biggl{(}\frac{BW\varphi}{2}\biggr{)} \tag{2}\]  
\[\theta c=\tan^{-1}\bigl{(}\tan\biggl{(}\theta o-\frac{BW\theta}{2}\biggr{)}+ \frac{b}{2h}\bigr{)} \tag{3}\]  
\[Lmn=d1\sqrt{m^{2}+n^{2}-mn} \tag{4}\]  
\[d1=ro\sqrt{3} \tag{5}\]  
\[\alpha mn=\ \sec\ \biggl{(}\frac{2m-n}{2\sqrt{m^{2}-mn+n^{2}}}\biggr{)} \tag{6}\]  
where \(h\) denotes the UAV altitude in km, \(x\), \(y\) and \(z\) denote the cellular footprint in three dimensions, \(\theta o\) and \(\theta c\) denote beams oriented towards a cell, \(a\) and \(c\) denote a major axis, while \(b\) and \(e\) denote a minor axis, 3 dB beam width in the elevation plane is denoted by \(BW\theta\) and the azimuth plane is denoted by \(BW\varphi\). In the cellular layout geometry, \(m\) and \(n\) denote cell coordinates [47]. The radial distance \(dm\) denotes a distance from the center cell that is below an UAV \(Lmn\) to the next cell by an azimuth angle \(\alpha mn\) that is measured from the vertical axis \(m\), \(ro\) denotes the central cell radius. \(m\) and \(n\) are considered only in the first sector of the tier, whereas for other cells this is located by rotating the structure by \(60^{\circ}\) with the same central distance [48].
## 3 Analysis and Results
### Analysis of Realistic EEG in Mental Arithmetic Tasks
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**CBTN**} & \multicolumn{2}{c}{**CPE**} \\ \hline  & **ACE** & **GNE** & **ACE** & **GNE** \\ \hline Subject 0 & 0 & 0 & 0 & 0 \\ Subject 1 & 0 & 0 & 0 & 0 \\ Subject 2 & 0.004 & 0.001 & 0 & 0 \\ Subject 3 & 0 & 0 & 0 & 0 \\ Subject 4 & **0.973** & **0.540** & **0.665** & 0 \\ Subject 5 & 0 & 0 & **0.518** & **0.342** \\ Subject 6 & 0 & 0 & 0 & 0 \\ Subject 7 & 0 & 0 & 0 & 0 \\ Subject 8 & 0 & 0 & 0 & 0 \\ Subject 9 & 0 & 0 & 0 & 0 \\ Subject 10 & 0 & 0 & 0 & 0 \\ Subject 11 & 0 & 0 & 0 & 0 \\ Subject 12 & 0.005 & 0.018 & 0 & 0 \\ Subject 13 & 0 & 0 & 0 & 0 \\ Subject 14 & **0.646** & **0.832** & 0 & 0.018 \\ Subject 15 & 0 & 0 & 0 & 0 \\ Subject 16 & 0 & 0 & 0 & 0 \\ Subject 17 & **0.125** & **0.158** & **0.398** & 0.035 \\ Subject 18 & 0 & 0 & 0 & 0 \\ Subject 19 & 0 & 0 & 0 & 0 \\ Subject 20 & 0 & 0 & 0 & 0 \\ Subject 21 & 0 & 0 & 0 & 0 \\ Subject 22 & 0 & 0 & 0.001 & 0.021 \\ Subject 23 & 0 & 0 & **0.186** & **0.680** \\ Subject 24 & 0 & 0 & 0.005 & 0.004 \\ Subject 25 & 0.011 & 0.007 & **0.483** & **0.603** \\ Subject 26 & 0 & 0 & 0.049 & **0.08** \\ Subject 27 & 0 & 0 & 0 & 0 \\ Subject 28 & 0 & 0 & 0 & 0 \\ Subject 29 & 0 & 0 & 0 & 0 \\ Subject 30 & 0 & 0 & 0 & 0 \\ Subject 32 & 0 & 0 & **0.591** & **0.895** \\ Subject 33 & 0 & 0 & 0 & 0 \\ Subject 34 & 0 & 0 & 0 & 0 \\ Subject 35 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}  
* ACE (average clustering coefficients), GNE (global network efficiency). Non significant results are shown in bold.  
\end{table} 
Table 1: Results of nonparametric permutation tests for each subject’s feature sequences in the resting and arithmetic states (1000 repeated arrangement sampling, significant level \(p=0.05\)).
## 3 Methodology for Data Pre-Processing
### Training Progress  
The input data obtained from the pre-processing steps were divided into batches, in order to reduce the memory size required for each training iteration. An epoch is considered complete when every batch has been processed once in an iteration. Figure 3a shows the loss and Figure 3b shows the validation loss over the trained epochs. As the loss decreases steadily, the model keeps on learning the input data inter-relations. Overfitting is indicated by an increasing validation loss beyond Epoch 60. The loss metrics of the test set had their lowest point in Epoch 35.
## 4 Experiment Design and Comparative Analysis
### Feature Selection  
To reduce the complexity of the wind power prediction model, this paper employs the Pearson correlation coefficient to analyze the factors influencing wind farms. The Pearson correlation coefficient measures the degree of linear correlation between two characteristic variables. It is calculated by the ratio between the covariance and the standard deviation of the two variables. A correlation coefficient of 1 indicates a perfect positive correlation, \(-1\) indicates a perfect negative correlation, and 0 indicates no correlation between the two variables. As shown in Figure 7, a darker color indicates a stronger correlation. It can be found that the wind power is strongly correlated with the wind speed, generator speed, impeller speed, and theoretical power, but there is a weak correlation with the wind angle, yaw angle, and absolute wind direction, and a negative correlation with the ambient temperature and cabin temperature. This article selects wind power, wind speed, wind direction, generator speed, impeller speed, cabin temperature, and environmental temperature as the input features of the model. For example, an increase in the wind speed corresponds to an increase in power. When the wind speed reaches the rated wind speed, the wind power will also correspondingly reach the rated power because the wind speed significantly impacts the wind power. For the other features, they also have a certain impact on the wind power, and using multiple features as inputs for the prediction model can make the model's prediction more effective.
# Predicting C/N0 as a Key Parameter for Network RTK Integrity Prediction in Urban Environments
## 4 Predicting Signal Strength
### Diffraction  
Diffraction loss (\(L_{D}\)) is calculated using the Knife Edge Diffraction (KED) model [52],  
\[L_{D}(\
u)=\frac{1}{1-j}\bigg{(}F(\
u)+\frac{1-j}{2}\bigg{)},\
u=b\sqrt{\frac{2}{ \lambda r_{2}}}, \tag{15}\]  
in which \(F(\
u)\) is the Fresnel integral, b is the deviation of the edge of the building from LOS, and \(r_{2}\) is the distance from the diffracting edge to the receiving antenna [53]. The diffraction loss is then reduced from the nominal value to obtain the C/N\({}_{0}\) value. It can be mentioned here that in this study, the diffraction is calculated considering the third Fresnel zone [41].
# Feature-Attended Federated LSTM for Anomaly Detection in the Financial Internet of Things +
## 6 Simulation
### Evaluation Metrics  
In order to verify the anomaly detection results of the proposed algorithm, there are three metrics of classification tasks are comprehensively used in the simulation. The first is accuracy, denoted as \(A\), which can directly measure the overall correctness including the correct identification of both anomalies and normal instances. The second metric is the area under the curve (AUC), representing the area under the receiver operating characteristic (ROC) curve, which assesses the algorithm's ability to discriminate between classes at various thresholds. A higher AUC indicates better algorithm performance, crucial for effective anomaly detection in dynamic scenarios. The last metric is the true positive rate, which indicates the proportion of actual anomalies correctly identified by the algorithm. This is crucial for financial systems, where missing an anomaly can have severe consequences. The accuracy is calculated as follows:  
\[A=\frac{TP+TN}{TP+FP+FN+TN} \tag{21}\]where \(TP\) represents the number of correctly classified target samples, \(TN\) represents the number of correctly classified other samples, \(FP\) represents the number of incorrectly identified target samples and \(FN\) represents the number of target samples that were missed.
# A Wideband Eight-Port MIMO Antenna with Reduced Mutual Coupling for Future 5G mm-Wave Applications
###### Abstract  
An eight-element MIMO antenna with a neutralization line was utilized for future 5G mm-wave applications. The MIMO configuration was designed for two ports, four ports and eight ports to validate the desired impedance and radiation characteristics. The measured results in terms of MIMO and scattering parameters correlate well with the simulated one. The printed eight-port antenna was a miniaturized size of 44 \(\times\) 70 \(\times\) 0.8 mm\({}^{3}\). Roger RT/duroid 5880 substrate was used to print antennas. The presented antenna produced a vast bandwidth of 18 GHz, varying from 28 to 46 GHz, and achieved a reduced mutual coupling of 30 dB with 6.8-8.5 dBi gain. The eight-port antenna is compared with contemporary antennas considering size, isolation, impedance bandwidth, diversity characteristics and radiation properties, confirming that the presented antenna is a promising candidate for future 5G mm-wave applications.  
MIMO; mm-wave; isolation; diversity characteristics; gain +
Footnote †: journal:  
Article
## 3 Results  
The integration of virtual reality and brain-computer interfaces has been explored in various research studies. Brain-computer interfaces have the potential to serve as a powerful control mechanism for a variety of applications by allowing direct communication between the human brain and external devices. When integrated with virtual reality environments, BCIs can enable users to influence navigation, communication, and other assistive functions through intuitive, multisensory interactions that engage the user and enhance their motivation and engagement. The immersive and interactive nature of virtual reality can amplify the capabilities of BCIs, providing a rich, context-specific interface for users to control digital systems and environments using their neural activity.  
This research builds upon our previous study published in [4], which explored the potential of virtual reality to enhance the usability and efficiency of brain-computer interfaces. Our team designed a custom computer system that enabled a participant to draw shapes on a computer screen using a BCI. The study employed a two-day within-subject design. On the first day, the participant completed drawing tasks without VR. On the second day, they performed the same tasks while immersed in a virtual environment using Esperanza EMV300 mobile VR goggles (Esperanza sp. j., Ozarow Mazowiecki, Poland). Our findings demonstrated a significant improvement in task performance when VR was integrated with the BCI. The average time taken to complete the drawing tasks decreased threefold in the VR condition compared to the non-VR condition. This suggests that VR immersion enhances concentration and facilitates more efficient BCI control. Furthermore, the study found that a task requiring high precision was notably easier for the participant to perform in the VR environment. These results, while based on a single-case study, highlight the potential of VR to improve the usability and effectiveness of BCI systems, particularly for tasks demanding focused attention and precise control.
## 4 Case Study and Discussion
The LOLE spikes to 314.23 h per year, and the LOLP increases substantially to 0.04646, suggesting that the system is highly prone to frequent shortages. The EENS also increases significantly to 2423.14 MWh, with an associated cost of USD 8.48 \(\times\) 10\({}^{6}\), and the reliability drops to 0.99599, underscoring the severe energy shortfalls and the technical and financial implications of the reduced CPP generation without renewable energy support. Table 4 evaluates system performance over a one-month period using various uncertainty modeling techniques, focusing on the impacts of 20% and 30% reductions in CPP generation. The real case, which excludes uncertainty, provides a baseline with key metrics like LOLE, LOLP, and EENS, along with EENS cost. In the 20% reduction scenario, the real case shows a highly reliable system with an LOLE of 0.70 h, LOLP of 0.00114, and EENS of 0.77 MWh, with an associated cost of USD 2.69 \(\times\) 10\({}^{3}\). Monte Carlo simulation combined with K-Means closely matches the real case, with an EENS of 0.83 MWh and a cost of USD 2.92 \(\times\) 10\({}^{3}\). MCMC, however, underestimates vulnerability, predicting an LOLE of 0.48 h and EENS of just 0.02 MWh, with an associated cost of USD 0.09 \(\times\) 10\({}^{3}\). In the 30% reduction scenario, the real case shows significant vulnerability, with an LOLE of 49.26 h and EENS of 90.21 MWh. Monte Carlo with K-Means performs well, showing an LOLE of 49.13 h and an EENS of 89.25 MWh. MCMC underestimates risks, while IGDT and robust optimization methods present more conservative estimates, with IGDT predicting an EENS of 93.47 MWh and the robust optimization method estimating 130.50 MWh, reflecting their cautious approach. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **20\% Reduction in the** & **30\% Reduction in the** & **Generation of CPPs** \\ \hline LOLE & & \\ [hours per year] & 14.77 & 314.23 \\ \hline LOLP & 0.00502 & 0.04646 \\ \hline EENS [MWh] & 13.86 & 2423.14 \\ \hline EENS cost [USD] & 4.85 \(\times\) 10\({}^{4}\) & 8.48 \(\times\) 10\({}^{6}\) \\ \hline Reliability & 0.99969 & 0.99599 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the system performance without the wind farm and with reductions of 20% and 30% in the generation of CPPs. Table 5 extends the system performance evaluation over a three-month period. The real case shows a low LOLE of 0.70 h per year for the 20% reduction scenario and 49.26 h per year for the 30% reduction scenario. Monte Carlo simulation combined with K-Means closely follows the real case, while MCMC underestimates risks. IGDT and robust optimization methods provide more conservative estimates, particularly in the 30% reduction scenario, where they predict higher LOLE and EENS values. While the numerical differences between methods may appear minor, they hold significant implications for power system reliability and operational decision-making.
## 3 Results and Discussion
### PST Removal from G. catenatum Cultures
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Toxin**} & **T0** & **T1** & **T2** & **T3** & **T4** & **T5** & **C** & **Removal** \\  & **(0 h)** & **(1 h)** & **(3 h)** & **(19 h)** & **(26 h)** & **(48 h)** & **(48 h)** & **(48 h, \%)** \\ \hline \multirow{2}{*}{C1,2} & 0.24 \(\pm\) & 0.25 \(\pm\) & 0.24 \(\pm\) & 0.25 \(\pm\) & 0.26 \(\pm\) & 0.25 \(\pm\) & 0.25 \(\pm\) & \multirow{2}{*}{—} \\  & 0.01 & 0.01 & 0.00 & 0.01 & 0.01 & 0.00 & 0.01 & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} DcSTX \\ * \\ \end{tabular} } & 0.33 \(\pm\) & 0.14 \(\pm\) & 0.12 \(\pm\) & 0.06 \(\pm\) & \multirow{2}{*}{ND} & \multirow{2}{*}{ND} & \multirow{2}{*}{ND} & 0.33 \(\pm\) & \multirow{2}{*}{100 \(\pm\) 0} \\  & 0.00 & 0.15 & 0.13 & 0.11 & & & & 0.01 \\ \hline \multirow{2}{*}{GTX5} & 0.09 \(\pm\) & 0.06 \(\pm\) & 0.06 \(\pm\) & 0.09 \(\pm\) & 0.06 \(\pm\) & 0.05 \(\pm\) & 0.09 \(\pm\) & \multirow{2}{*}{55 \(\pm\) 5} \\  & 0.00 & 0.00 & 0.01 & 0.00 & 0.02 & 0.02 & 0.00 & \\ \hline \multirow{2}{*}{GTX6} & 0.57 \(\pm\) & 0.58 \(\pm\) & 0.56 \(\pm\) & 0.58 \(\pm\) & 0.57 \(\pm\) & 0.56 \(\pm\) & 0.57 \(\pm\) & \multirow{2}{*}{—} \\  & 0.02 & 0.01 & 0.01 & 0.02 & 0.01 & 0.02 & 0.03 & \\ \hline \hline \end{tabular} \({}^{*}\)For the calculation of the average and standard deviation, the value of zero was assigned when the toxins were not detected.  
\end{table} 
Table 1: PST concentrations, in uM (average \(\pm\) standard deviation, SD), in _G. catenatum_ cultures during the removal studies using the H-form resin. At time 0 and 48 h control, no resin was present. fact, a similar behaviour was observed by other authors [38]. These authors tested the adsorption of eleven PST to magnetic nanoparticles. From the experiments, they reported a very low removal for the toxins C1 and GTXs (GTX1-6 and dcGTX2-3), in contrast with STX, dcSTX, NEO, and dcNEO, attributing the low adsorption/removal of those toxins to the presence of the sulphate side groups in their structure.
# Algorithm for Fatigue Crack Initiation Assessment Based on Industrial Photogrammetry
## 1 Introduction
Some scientific approaches for monitoring the initiation and growth of fatigue cracks refer to the use of other types of cameras or sensors, e.g., infrared thermography [20; 21], a combination of ultraviolet and visible band sensor systems [7], entropy of acoustic emission signals [22; 23; 24], and ultrasonic signals [25]. Industrial photogrammetry and feature matching approaches [26; 27] have been used for robust and visual assessment of crack initiation. In a previous study, weld monitoring was successfully achieved by macro-photogrammetry techniques based on an approach developed in Matlab [28]. One of the most commonly used methods for feature matching in computer vision problems is Oriented FAST and Rotated BRIEF (ORB) [18]. ORB is one of the fastest feature-matching algorithms and outperforms the Speeded-Up Robust Feature (SURF), Scale-Invariant Feature Transform (SIFT), and Features from Accelerated Segment Test (FAST) algorithms in terms of execution speed [29]. In addition, the ORB method has two major advantages: it allows the evaluation of feature matching using the Harris Corner score to collect quality points, and ORB features are invariant to scaling, rotation, and limited affine changes [30]. In this paper, a novel algorithm has been developed specifically for the detection of fatigue crack initiation period during a fatigue test using industrial photogrammetry. Our approach uses advanced image processing techniques and computer vision algorithms to analyze high-resolution photographs of stressed materials and detect early signs of fatigue in the form of crack initiation. This algorithm fully automatically detects the time of crack initiation in the tested steel specimens and significantly shortens the inspection time compared to conventional and visually based inspection methods. The impact of this research on improving the reliability, efficiency, and cost-effectiveness of materials testing is profound and represents a significant contribution to the field of materials science and civil engineering.
# A Deep Multi-Frame Super-Resolution Network for Dynamic Scenes
## 3 Methods
### Spatial Subpixel Alignment Module  
Commonly, most learning-based methods align the input frames to the target frame on a low-resolution grid. This kind of operation may result in a loss of subpixel information, which eventually affects the restoration. To solve this problem, we propose an SSA module to get more accurate alignment. The SSA module provides subpixel-wise alignment, which is processed with the aid of optical flow.  
We aim to align features in high-resolution for more accurate alignment. First, we upsample the dense optical flow and the features into a high-resolution grid. The optical flow with a resolution of (H, W) is resized to the resolution of (H \(\times\) r, W \(\times\) r), where H, W and r represent the image height, the image width and the image upscaling factor, respectively. Similarly, the features with the size of (m \(\times\) r \(\times\) r, H, W) is resampled to the size of (m, H \(\times\) r, W \(\times\) r) through pixel shuffle operation, where m indicts the number of feature groups and each group has r+r features. Different from resizing, pixel shuffle rearranges the values along the dimension of different features to a high-resolution grid, keeping the total number of elements unchanged. Then a backward warping operation is applied with both optical flow and features to generate aligned features with the size of (m, H \(\times\) r, W \(\times\) r). Finally, aligned features on the HR grid are converted into low-resolution features with the size of (m \(\times\) r \(\times\) r, H, W) by a reverse pixel shuffle operation. In this paper, we set m to 8. A simple example is shown in Figure 3, where m = 1, r = 2.  
The basic reason for such a design is that we choose pixel shuffle operation in our tail part, which is a common choice. It is clear that low-resolution features in each channel had a subpixel relationship with other channels because they are adjacent to each other after pixel shuffle. As a result, any subpixel operation would modify all the channels of features, and low-resolution features in our model should be determined by all the channels of the former features. Under this premise, if we directly warp on a low-resolution grid for alignment, each new channel is determined by one channel, which will cause the loss of subpixel information.  
Figure 3 shows a simple case that how the subpixel operation affects all channels. For simplicity, four channels of features in red, yellow, blue and green are planned to move 0.5 pixels in both horizontal and vertical direction, which is exactly 1 pixel in both directions on \(\times\)2 resolution. After applying the SSA module, the color order turns to green, blue, yellow and red. This proves the cross-channel effect caused by optical flow.  
Figure 3: The architecture of the spatial subpixel alignment (SSA) module.
## 2 Proposed Method
### Multi-Objective Instance-Weighting Strategy
#### 2.5.2 Maximum Mean Discrepancy  
The MMD is the distance between the two domain distributions calculated under the RKHS. Note that the above-mentioned KMM procedure derives the instance weights by minimizing the MMD between domains. The MMD is also used to measure domain discrepancy in transfer learning and is formulated as follows:  
\[MMD(X_{S},X_{T})=\parallel\frac{1}{n_{S}}\sum_{i=1}^{n_{S}}\psi\left(x_{S_{i}} \right)-\frac{1}{n_{T}}\sum_{j=1}^{n_{T}}\psi\left(x_{T_{j}}\right)\parallel_ {\mathcal{H}}^{2}, \tag{7}\]  
where \(\psi\) is the kernel mapping function, which converts original feature space into RKHS. We derive the MMD using the Gaussian radial basis function (RBF) kernel in the form of a nonlinear kernel. Reference paper has confirmed that this kernel function is suitable for the fault diagnosis, which is the goal of this research [39]. The MMD is used to measure the dissimilarity between domain instances because it can obtain the distance between two probability distributions in a nonparametric method without dimension constraints. Intuitively, because it is the squared distance of the mean of domain instances, it has a value close to 0 if the data are drawn from the same distribution. We derive the MMD of each source instance with the entire target domain data in order to use it as the weight. The MMD between \(i^{th}\) source domain instance and entire target domain instance is calculated as follows [40; 41]:  
\[MMD_{i}\left(x_{S_{i}},X_{T}\right)=\parallel\psi\left(x_{S_{i}}\right)-\frac {1}{n_{T}}\sum_{j=1}^{n_{T}}\psi\left(x_{T_{j}}\right)\parallel_{\mathcal{H}} ^{2} \tag{8}\]  
Source domain instances which have a distribution similar to the target distribution have low MMD values. The source domain instance with low MMD is suitable for an auxiliary training sample for the target domain task.