esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Study
### Results and Analyses
Aside from that, the trade-off parameters of HFLRSC play roles in balancing the low rank part of the model. Because of the efficiency of HFLRSC, the values of these parameters are often set within a large range, and the best parameter value with the best result is selected by cycling all the parameter values. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Datasets** & **Methods** & **ACC** & **NMI** & **Purity** & **F** & **ARI** \\ \hline \multirow{8}{*}{8} & HFLRSC & **95.23** & **93.75** & **95.23** & **89.61** & **88.75** \\  & NCut & 59.30 & 66.47 & 61.17 & 49.10 & 44.68 \\  & K-means & 48.69 & 50.90 & 51.68 & 32.89 & 26.73 \\  & LRR & 85.78 & 86.94 & 85.83 & 76.80 & 74.85 \\  & SSC & 75.50 & 73.28 & 75.50 & 52.70 & 48.12 \\  & SC-SRGF & 71.47 & 71.77 & 72.40 & 55.89 & 52.17 \\  & USENC & 54.67 & 57.16 & 56.46 & 37.81 & 32.37 \\ \hline \multirow{8}{*}{9} & HFLRSC & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** \\  & NCut & 91.37 & 95.97 & 92.83 & 91.63 & 90.87 \\  & K-means & 71.67 & 84.18 & 74.62 & 71.70 & 68.83 \\  & LRR & 99.12 & 99.63 & 99.33 & 99.17 & 99.09 \\  & SSC & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** \\  & SC-SRGF & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** \\  & USENC & 98.46 & 98.94 & 98.67 & 98.02 & 97.84 \\ \hline \multirow{8}{*}{10} & HFLRSC & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** \\  & NCut & 82.82 & 89.03 & 85.10 & 80.15 & 78.36 \\  & K-means & 57.20 & 63.51 & 61.38 & 44.61 & 38.83 \\  & LRR & 98.90 & 99.54 & 99.17 & 98.95 & 98.86 \\  & SSC & 95.83 & 95.31 & 95.83 & 91.69 & 91.00 \\  & SC-SRGF & 89.93 & 89.72 & 89.95 & 83.10 & 81.70 \\  & USENC & 88.79 & 89.46 & 89.63 & 82.50 & 80.99 \\ \hline \multirow{8}{*}{11} & HFLRSC & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** \\  & NCut & 71.45 & 76.92 & 72.77 & 63.55 & 60.35 \\ \cline{1-1}  & K-means & 52.20 & 55.65 & 55.89 & 36.86 & 30.48 \\ \cline{1-1}  & LRR & 89.37 & 89.41 & 90.38 & 80.02 & 78.26 \\ \cline{1-1}  & SSC & 87.83 & 85.71 & 87.83 & 76.05 & 73.98 \\ \cline{1-1}  & SC-SRGF & 83.50 & 84.49 & 84.73 & 75.85 & 73.83 \\ \cline{1-1}  & USENC & 81.46 & 81.76 & 82.79 & 70.22 & 67.62 \\ \hline \multirow{8}{*}{12} & HFLRSC & **99.17** & **98.88** & **99.17** & **98.24** & **98.10** \\  & NCut & 58.32 & 63.05 & 60.10 & 45.04 & 40.29 \\ \cline{1-1}  & K-means & 46.56 & 48.47 & 50.24 & 29.39 & 22.36 \\ \cline{1-1}  & LRR & 83.68 & 83.71 & 83.92 & 69.75 & 67.04 \\ \cline{1-1}  & SSC & 85.58 & 82.37 & 85.58 & 70.23 & 67.62 \\ \cline{1-1}  & SC-SRGF & 75.73 & 73.50 & 75.98 & 59.88 & 56.50 \\ \cline{1-1}  & USENC & 46.96 & 48.12 & 48.54 & 27.86 & 21.27 \\ \hline \end{tabular}
\end{table} 
Table 2: Cont.
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 2 State-of-the-Art in Non-Uniform and Low Latency Resampling
### Resampling by Kriging  
Kriging was developed in geological science to interpolate mineral concentrations in the soil from several probe points in a 2- or 3-dimensional space [18]. Kriging can also be applied to interpolate over time as a single dimension instead of geometrical dimensions. Kriging is based on the variogram function, giving the expected average squared difference of the measured value between two points as a function of their distance. Kriging and the similar Gaussian Process Regression have not been widely used for resampling. Bothmethods can provide prediction error estimates for the area between measurement points. This feature is applied in [19] to select points with high predicted error as additional sampling points. A special case of image processing is handled in [20]. They demonstrated how the covariance matrix could be constructed in polar coordinates. Neither of these papers can be compared to the suggested new Kriging application for resampling in the time domain.
# A Mobile Solution for Enhancing Tourist Safety in Warm and Humid Destinations
## 2 Literature Review
### Significance of Mobile Applications in Risk Avoidance and Tourism Safety  
The available literature underscores the significance of effective risk management strategies for safeguarding the health and safety of tourists. However, a notable research gap exists in the development of holistic mobile applications tailored to address the specific requirements of warning tourists about the risks of a heat-related illness at their intended attractions, monitoring their well-being while on-site, and providing prompt rescue assistance during urgent situations.  
While scholars have explored the utilization of mobile and web-based applications to enhance tourist safety across various destinations, their focus has predominantly centered on crisis communication, preparedness for natural disasters, and urban safety [20; 33; 34; 35; 36; 37; 38]. Some attention has also been given to the integration of smartwatches and wearable devices in managing tourist risks and safety, facilitating real-time monitoring, personalized recommendations, risk assessment, and emergency support [39; 40; 41; 42; 43]. In the realm of tourism app development, researchers [44] delved into crafting an interactive design that uniquely catered to enhancing safety measures at various tourist destinations. Their innovative approach not only focused on preventive strategies but also integrated crucial first-aid assistance to promptly address any potential criminal incidents.  
Furthermore, in the pursuit of ensuring traveler safety on the road, [PERSON] et al. [45] introduced a revolutionary mobile application named \"Journey Safety\". This cutting-edge app aims to offer travelers comprehensive safety provisions and timely aid throughout their journeys, guaranteeing a secure and worry-free travel experience like never before.  
These investigations underscore the immense potential of technology-driven solutions in bolstering tourist safety and enhancing their overall experience. However, to address the research gap effectively, further exploration and advancement are warranted within the tourism industry to develop all-encompassing mobile applications that cater specifically to the aforementioned needs of warning, monitoring, and rescuing tourists regarding heat-related illness risks. This endeavor would considerably contribute to the advancement of tourist safety measures, ensuring a more gratifying and secure experience, especially in unfamiliar environments.
###### Abstract
The studies test the feasibility of modeling reverberated audio for several different frequency bands by training a model for only one band, thus trying to avoid the need to train different models for each frequency band separately. We have chosen recurrent neural networks (RNNs) for this task because they could be good for modeling reverberating audio, as they are designed to handle sequential data, allowing them to account for the time-varying nature of audio signals, and their internal memory cells can effectively capture the dependencies between successive audio samples, leading to a more accurate representation of reverberation characteristics. The bidirectional LSTM, LSTM, and GRU recurrent neural network architectures offers unique strengths and trade-offs in terms of modeling capacity, computational efficiency, and memory requirements, and a thorough evaluation can help identify the most suitable approach for capturing the complex temporal relationships present in reverberating audio signals, ultimately leading to better performance and practical applicability. The structure of the article is as follows: Section 2 presents the dataset and the methods used in our study. The preparation of the dataset is described in detail in Section 2.1. Section 2.2 provides a detailed explanation of our method, which compared three recurrent neural network structures that attempted to predict room reverberation for each octave band. Section 3 describes our experimental setup and a comparison of the reverberation prediction results using different recurrent neural network models. Section 4 provides a discussion and concludes the results of our study.
## 4 Results and Discussion
### EEG Data Results
#### 4.2.2 Working Memory Results
The TE\({}_{\text{ex}}^{\theta}\)-based classifier has average accuracies of \(0.94\pm 0.04\), \(0.92\pm 0.08\), and \(0.93\pm 0.08\) for the subjects in G. I, G. II, and G. III, respectively. Lastly, Figure 13 shows the average nodal relevance, as defined by \(\varrho_{n}\), and the most relevant connectivities for each group, discriminated by frequency band. For G. I we observe widespread high node relevance in both the \(\alpha\) and \(\beta_{l}\) bands and low node relevance in the \(\theta\) band. Most relevant connections are present in the \(\beta_{l}\) band with many connections originating in the parieto-occipital region and targeting frontal and centro-frontal areas. For G. II and G. III node relevance is more evenly distributed across the three frequency bands considered. Spatially, it is more prominent around some pre-frontal, frontal, centro-parietal, and parietal nodes. In terms of the most relevant connections, we observe long-range contralateral interactions involving mostly the regions previously listed, as well as some connections to and from temporal areas. Therefore, we argue that the information flow between frontal, parietal, and temporal regions, coded in the phases of oscillatory  
Figure 11: Highest average classification accuracy for each subject in the WM database. The subjects are ordered from highest to lowest performance according to the accuracies obtained for the TE\({}_{\text{ex}}^{\theta}\)-based classifier. activity in the \(\theta\), \(\alpha\), and \(\beta_{l}\) bands, is what allowed us to discriminate among different memory loads from TE\({}_{\text{ex}}^{\theta}\) features. These results agree with several studies that identify fronto-parietal and fronto-temporal neural circuits operating in frequency ranges spanning from \(\theta\) to \(\beta\) as key during the activation of working memory [35; 50; 51].
## 3 Results and Discussion
### Optoelectrical Properties of Embedded Cu Mesltes
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Coating** & \(R_{s}\)**, \(\Omega\)/sq** & **T (550 nm), \%** & \(\sigma_{dc}\)/\(\sigma_{opt}\)** & **Reference** \\ \hline AZO/Ag/AZO & 7 & 90 & 491.5 & [32] \\ AZO/Ag/TiO\({}_{2}\) & 5.75 & 91.6 & 730 & [33] \\ Ag nanowires & 5 & 92 & 884.9 & [34] \\ PDDA/Ag nanowires composite & 22 & 95.5 & 443 & [35] \\ PEDOT:PSS/Ag nanowires & 3 & 81.1 & 1131.1 & [36] \\ Cu nanowires & 51.5 & 93.1 & 98.2 & [37] \\ Ag mesh (PL template) & 8.2 & 80.2 & 197 & [38] \\ AZO/Cu mesh (PL template) & 6.197 & 90.657 & 605.3 & [39] \\ Au mesh (cracked template) & 3.2 & 92 & 1386.1 & [40] \\ AgNP mesh (Emulsion template) & 8.2 & 88 & 347.7 & [24] \\ Cu mesh (Biomimic Vein-like template) & 0.1 & 80 & 15,708 & [41] \\ e-LCM 30 & 2.43 & 91.2 & 1611.1 & \\ e-LCM 180 & 0.21 & 84.3 & 10,189.2 & This work \\ e-SCM 30 & 0.53 & 73.8 & 2179.2 & \\ e-SCM 180 & 0.049 & 63.2 & 14,842.5 & This work \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of optoelectronic parameters of various transparent conductive coatings. Figure 8: Optical transparency (**a**); sheet resistance measured over the entire area of a 2.5 \(\times\) 3 cm\({}^{2}\) sample (**b**). Based on the data in Table 2, it can be concluded that in terms of optoelectronic characteristics, e-LCM 30 and e-LCM 180 coatings have great potential in transparent EMI shielding and other important applications, for example, transparent heaters, transparent electrodes for LEDs or OLEDs, photovoltaic cells, etc.
#### 1.1.5 Climate and Season Extension  
Based on the climate and season extension, greenhouses can be divided into two main groups, including (i) seasonal greenhouses and (ii) year-round (perennial) greenhouses. The seasonal greenhouses extend the growing season and protect during colder months. However, the year-round (perennial) greenhouses are designed to operate throughout the year, enabling continuous crop cultivation.
# Antiderivative Antialiasing for Stateful Systems +
## 1 Introduction  
Nonlinear systems play an important role in musical signal processing. In particular, there are many effects categorized as overdrive, distortion, or fuzz, whose primary objective is to introduce harmonic distortion to enrich the signal. Usually, the nonlinear behavior is combined with (linear) filtering to spectrally shape the output signal or to make the amount of introduced distortion frequency-dependent. While many of these systems were originally designed in the analog domain, naturally, there is interest in deriving digital models for them, e.g., [1; 2; 3; 4].  
One major problem encountered in digital nonlinear systems, whether designed from scratch or derived by virtual analog modeling is aliasing distortion. Once the additional harmonics introduced by the nonlinearity exceed the Nyquist limit at half the sampling frequency, they get folded back to lower frequencies, just as if the corresponding analog signal had been sampled without appropriate band-limiting. Contrary to the desired harmonic distortion, aliasing distortion is usually perceived as unpleasant. Therefore, methods to suppress or reduce aliasing distortion are needed.  
The conceptually simplest aliasing reduction method is oversampling. However, if the harmonics decay slowly with frequency, the oversampling factor has to be high, making the approach unattractive due to the rising computational demand. Consequently, various alternatives have been proposed, e.g., [5; 6; 7; 8; 9; 10; 11]. These methods, however, usually come with certain limitations, most commonly the restriction to memoryless systems. In this work, an extension of [10] is presented that loosens the restriction from memoryless systems to a certain class of stateful systems.  
This article is an extension of our previous conference paper [12]. The main differences are the generalization of the method to the case of multiple parallel nonlinear functions and an added discussion of nonlinearities with vector-valued input.
## 3 Ranging Error Source Analysis and Modeling
### The Error Model of Ionospheric and Tropospheric  
Research on the error model of ionospheric and tropospheric propagation is relatively mature. Therefore, we refer to references [28; 29] to directly give the ionospheric and convective error correction model as follows:  
\[\sigma_{\text{Ionosphere}}\sim\left[1-(\frac{R_{e}\cos\varphi}{R_{e}+h_{ave}})^{ 2}\right]^{-1/2} \tag{4}\]  
\[\sigma_{\text{Troposphere}}\sim\left[1-(\frac{\cos\varphi}{1.001})^{2}\right]^{ -1/2} \tag{5}\]  
where \(R_{e}\) is the average radius of the Earth and \(h_{ave}\) is the average height of the ionosphere. \(\varphi\) is the elevation angle of the satellite.
## 4 V2H and V2G
### Technological Developments  
Denmark, the Netherlands, the U.S., Japan, and Hong Kong are the leading countries and regions developing V2G and V2H technologies. Typical V2G and V2H technologies and products in these regions may provide references for the future development of other countries and regions.
## 3 Results and Discussion
### Ni Renoval Mechanism
#### 3.6.2 FTIR  
Figure 11 shows the FTIR spectra of ATP, nZVI@ATP, and nZVI@ATP after the reaction with Ni. The wavelength broadband of ATP at 3404 cm\({}^{-1}\) was related to the vibrational peak of -OH [33]. After loading the nZVI, the stretching vibrational peak of -OH shifted to 3150 cm\({}^{-1}\), indicating that the nZVI@ATP generated iron oxide during the preparation process [24]. After the reaction of the nZVI@ATP with Ni, the stretching vibration peak of -OH weakened considerably in intensity, indicating that the material was complexed with Ni\({}^{2+}\)[35]. The vibrational peak at 1630 cm\({}^{-1}\) is C=O in the material; the telescopic vibrational peak at 1380 cm\({}^{-1}\) is the Fe-OH vibrational peak; and the characteristic peak at 1022 cm\({}^{-1}\) is the telescopic vibrational peak of the Si-O-Si bond in the material [36]. The stretching vibration peak at 670 cm\({}^{-1}\) was attributed to Fe-O, indicating that the material reacted with Ni to form iron oxides [21].
## 3 Gauss-Maxwell Beams
### Arbitrarily Polarized Gauss-Maxwell Beams  
In the previous section, for simplicity, we have considered a simple case of linear polarization at the input plane. As seen, the polarization of this beam changes with \(z\). Now we are going to extend the approach to a general input polarization state with the polarization plane perpendicular to the longitudinal direction. Such a state can be described by the superposition  
\[|P\rangle=\cos(\theta/2)|H\rangle+\sin(\theta/2)e^{i\phi}|V\rangle, \tag{71}\]  
with \(\theta\in(0,\pi)\) and \(\phi\in(0,2\pi)\) defined on the Poincare sphere [40], and where the vector states \(|H\rangle\) (\(|P\rangle\) for \(\theta=0\)) and \(|V\rangle\) (\(|P\rangle\) for \(\theta=\pi\)) denote, respectively, horizontal and vertical polarization with respect to the \(x\)-axis. For simplicity, from now on, we shall consider the coefficients \(\alpha=\cos(\theta/2)e^{-i\delta/2}\) and \(\beta=\sin(\theta/2)e^{i\phi+i\delta/2}\), which also include any possible relative phase shift imprinted on the state by some external action (e.g., as a result of a weak measurement). As it can be noticed, these coefficients satisfy the relation \(|\alpha|^{2}+|\beta|^{2}=1\) (unit radius on the Poincare sphere). To start with, let us consider the input electric and magnetic field vectors  
\[\mathbf{E}(\mathbf{r}_{\perp};0) = E_{0}\psi(\mathbf{r}_{\perp};0)\left( \begin{array}{c}\alpha\\ \beta\\ 0\end{array} \right),\] \[\mathbf{H}(\mathbf{r}_{\perp};0) = H_{0}\psi(\mathbf{r}_{\perp};0)\left( \begin{array}{c}-\beta\\ \alpha\\ 0\end{array} \right), \tag{72}\]  
which are expressed in terms of an arbitrary polarization vector field specified by the coefficients \(\alpha\) and \(\beta\) introduced above. The corresponding six-dimensional electromagnetic vector field at the input plane \(z=0\) is  
\[\mathbf{F}(\mathbf{r}_{\perp};0)=\sqrt{\frac{\epsilon_{0}}{2}}\ E_{0}\psi( \mathbf{r}_{\perp};0)\left( \begin{array}{c}\alpha\\ \beta\\ 0\\ -\beta\\ \alpha\\ 0\end{array} \right), \tag{73}\]  
which reduces to (47) when \(\alpha=1\). Taking into account how the different \(\mathbb{G}\)-matrices and their products operate over the column vector (73), the expression for the propagated polarized vector field becomes \[\mathbf{F}(\mathbf{r}_{\perp};z)=\sqrt{\frac{\epsilon_{0}}{2}}\;E_{0}\psi(\mathbf{r} _{\perp};z)\;\left( \begin{array}{c}\alpha-\alpha[(\Delta x)^{2}-(\Delta y)^{2} ]/2q_{z}^{2}-\beta\Delta x\Delta y/q_{z}^{2}\\ \\ \beta+\beta[(\Delta x)^{2}-(\Delta y)^{2}]/2q_{z}^{2}-\alpha\Delta x\Delta y/q_ {z}^{2}\\ \\ -(\alpha\Delta x+\beta\Delta y)/q_{z}\\ \\ -\beta+\beta[(\Delta x)^{2}-(\Delta y)^{2}]/2q_{z}^{2}-\alpha\Delta x\Delta y/q _{z}^{2}\\ \\ \alpha+\alpha[(\Delta x)^{2}-(\Delta y)^{2}]/2q_{z}^{2}+\beta\Delta x\Delta y/q _{z}^{2}\\ \\ (\beta\Delta x-\alpha\Delta y)/q_{z}\end{array} \right).
# Optimization of Coverage and Capacity Using Smart Antennae
## 4 Evaluation
### Discussion  
In this study, three simulation scenarios were analyzed. Unlike traditional omnidirectional antennas that can only modify signal coverage by adjusting base station power levels, this method introduces a novel approach. When compared with omnidirectional antennas, particularly in situations with higher user counts, such as 1200, the simulation results indicate an approximately 23.6% improvement in coverage rate. Moreover, with an increase in the number of base stations to 12, using omnidirectional antennas covered only 38.3% of the service area, while employing smart antennas enhanced the signal coverage rate to 78.1%. In larger testing areas, such as 1000 square meters, this method enhanced the field coverage rate by 42.8%.  
The experimental results underscore the capability of this approach to adapt the radiation pattern of base stations to serve a larger number of users effectively in relatively spacious environments, even when the number of base stations is limited. This stands in contrast to conventional methods that primarily rely on adjusting base station power. This study presents a promising solution for enhancing signal coverage and capacity in scenarios with varying user densities and base station limitations.
## 3 Materials and Methods
### System Architecture
#### 3.1.2 Multistage Feature Extraction  
Our model employs a multistage feature extraction approach, leveraging the hierarchical nature of the DenseNet-121 architecture. Features are extracted from different stages of the modified DenseNet-121, specifically after the second, third, and fourth dense blocks. This strategy allows the model to capture a diverse range of features, from low-level textures and edges to high-level semantic information. The multistage feature extraction enhances the model's ability to detect and classify surgical tools across various scales and complexities.
# Development of Modular Bio-Inspired Autonomous Underwater Vehicle for Close Subsea Asset Inspection
## 7 Initial Testing and Lessons Learned
### Testing Computer Vision  
In order to quantify the performance of the computer vision system, a dataset with ground truth was required. To the best of our knowledge, one of the only underwater datasets to provide a trajectory estimate is the AQUALOC dataset. This dataset provides an structure-from-motion trajectory calculated offline [42]. The assumption was then made that improvements in the accuracy of the PySLAM-based VO calculated using ORB features would result in improvements to ORB SLAM 2. A Python script was written to cycle through various OpenCV image processing techniques (e.g., histogram equalisation and image filtering) and multiple ORB and BRISK parameters to determine which combination produced the most accurate estimate of the camera's trajectory. This was determined using the mean squared error between the VO estimate and the structure-from-motion ground truth trajectory obtained from the AQUALOC dataset. A graph of the result of these tests with the most accurate configuration selected is shown in Figure 18.  
It was determined that the highest accuracy was achieved when using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and an ORB feature matcher with the following parameters: Edge Threshold and Patch Size of 30; Minimum FAST Threshold of 30; First Level of 4; Maximum ORB Features of 1500 and all others at default OpenCV values. The ORB SLAM 2 code was then modified to include CLAHE image processing and the calculated ORB feature-matching parameters. This was then compared against  
Figure 18: Results of Different Image-Processing Techniques and Feature-Matching Parameters on the Accuracy of VO Relative to the Structure-from-motion “ground truth”: the test with the smallest error is highlighted and the settings for that test displayed.  
a version of ORB SLAM 2 without CLAHE image processing and using ORB-SLAM 2's default ORB feature-matching parameters. Tests were conducted on both the AQUALOC and Marine Autonomous Robotics for InterventionS MARIS [58] underwater datasets. The modified ORB SLAM 2 appeared to yield improved SLAM accuracy, losing tracking a reduced number of times on each dataset. ORB SLAM 2 ran at usable framerates on a Raspberry Pi 4 of around 15-20 fps, suitable for slow-moving AUVs. It is recommended that ORB SLAM 2 with the provided settings be used as an initial platform on which to develop further underwater visual SLAM robotic applications.