esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 O-RAN Architecture and Development
### ML Workflow in O-RAN  
ML workflows in O-RAN involve a series of steps that enable the development, deployment, and optimization of ML models for network operations. The workflow consists of several stages of processing. We shall elaborate the processes involved in ML workflow in the following [22]:  
* Data collection: The first step in the ML workflow is to collect and preprocess the data. This involves identifying the relevant data sources, collecting the data, and preparing it for analysis. This step is crucial as the quality of the ML model depends on the quality of the data used to train it;
* Data exploration and analysis: In this step, the collected data are explored to gain insights and identify patterns. This involves data visualization, statistical analysis, and other data exploration techniques to understand the underlying structure of the data. This step is important for selecting appropriate ML algorithms and for identifying relevant features that can be used to train the models;
* Model development: In this step, ML algorithms are selected and trained using the data. This involves selecting the appropriate algorithms, feature engineering, and tuning the model hyper-parameters. Once the model is trained, it is evaluated and validated to ensure that it is accurate and reliable;
* Model deployment: In this step, the trained model is deployed into the O-RAN environment. This involves integrating the model into the network operations environment and deploying it in a way that allows it to access the relevant data and provide real-time predictions or recommendations. This step also involves monitoring the performance of the deployed model to ensure that it is performing as expected;
* Model optimization: Once the model is deployed, it needs to be optimized to improve its performance. This involves monitoring the performance of the model in real-time, identifying areas for improvement, and updating the model as necessary. This step is crucial for ensuring that the ML models continue to provide accurate and reliable predictions and recommendations over time;
* Model maintenance: The final step in the ML workflow is model maintenance. This involves maintaining the ML model, updating it as necessary, and ensuring that it remains aligned with the evolving needs of the O-RAN network operations environment.  
Overall, the ML workflow in O-RAN involves a series of steps that enable the development, deployment, and optimization of ML models for network operations. By leveraging the power of ML, network operators can improve network performance, reduce energy consumption, and provide a better user experience.
# Variable Rate Point Cloud Attribute Compression with Non-Local Attention Optimization
## 3 Point Cloud Attribute Variational Autoencoder  
Due to the good exploration of sparse convolution on the sparsity of point cloud, we use it as the basic layer of the proposed neural network. It only computes outputs on predefined voxels and saves them into a compact sparse tensor. The sparse tensor is represented by a set of coordinates \(\mathbf{\bar{C}}=\left\{\left(x_{i},y_{i},z_{i}\right)\right\}_{i}\) of these voxels and corresponding features \(\mathbf{\bar{F}}=\left\{\vec{f}_{i}\right\}_{i}\) which can be written in a simple form:  
\[\mathbf{\bar{C}}=\begin{bmatrix}x_{1}&y_{1}&z_{1}\\ &\vdots&\\ x_{N}&y_{N}&z_{N}\end{bmatrix},\mathbf{\bar{F}}=\begin{bmatrix}f_{11}&\cdots&f_ {1C}\\ \vdots&\ddots&\vdots\\ f_{N1}&\cdots&f_{NC}\end{bmatrix}=\begin{bmatrix}\vec{f}_{1}^{T}\\ \vdots\\ \vec{f}_{N}^{T}\end{bmatrix}, \tag{1}\]  
where \(N\) is the number of point/feature, and \(C\) is the number of channel. Then, sparse convolution can be defined as follows:  
\[\vec{f}_{u}^{\text{out}}=\sum_{i\in K^{3}\left(u,\mathbf{\bar{C}}^{in}\right) }W_{i}\vec{f}_{u+i}^{in}\quad for\quad u\in\mathbf{\bar{C}}^{out}, \tag{2}\]  
where \(\mathbf{\bar{C}}^{in}\) and \(\mathbf{\bar{C}}^{out}\) are input and output coordinates, respectively. \(\vec{f}_{u}^{in}\) and \(\vec{f}_{u}^{out}\) are input and output feature vectors at coordinate \(u\) (i.e., \((x_{u},y_{u},z_{u})\)), respectively. \(K^{3}\left(u,\mathbf{\bar{C}}^{in}\right)=\left\{i\mid u+i\in\mathbf{\bar{C}}^{ in},i\in K^{3}\right\}\) defines a 3D convolutional kernel centered at \(u\) with offset \(i\) in \(\mathbf{\bar{C}}^{in}\). \(\hat{W}_{i}\) is kernel weights.  
In 3D dense convolution, the convolution is implemented on each voxel in the 3D dense grid. If the centered voxel is empty and its 3D kernel covers any occupied voxel, the centered voxel will be filled by the value generated by the convolution, which leads to the occupied voxels gradually increase. In contrast, the sparse convolution works strictly on submanifolds of data, hence the generated values also only exist on the submanifolds. Therefore, after deep convolution layers, the point cloud structure could still remain the sparsity, avoiding the difficulty of compression caused by feature map dilation.
## 1 Introduction  
The study of flow around objects is a vital part of engineering design, providing the necessary input for a proper and sustainable design. In the case of aerodynamics, the shape and size of the wing play an integral role in its performance. By better understanding the flow around the wing, optimized structures can be made, resulting in better, cheaper, and safer airplanes. The flow around a wing is complex and much research has gone into understanding and predicting it with the constant increase in computational power, computers have been increasingly used to model such fluid flows. This field is known as Computational Fluid Dynamics (CFD), the basis of which are the Navier-Stokes equations, describing viscous fluid flow, and the Euler equations, describing perfect, inviscid flow [1]. As these equations are complex, or potentially impossible to solve analytically, numerical methods are used to get useful solutions. COMSOL Multiphysics is the software that is used to make and solve the simulations.  
In addition to the structure of a wing, the surface roughness of a wing plays a significant role in its drag coefficient [2; 3; 4; 5]. Good simulations could provide additional insights into the development of coatings and other materials of airplanes. Airplanes are not the only area that can benefit from accurate simulations of airfoils and wing structures. Such structures can also be found in the design of the blades in pumps, compressors, or turbines. CFD can be used to predict and optimize the performance of such devices [6; 7; 8]. CFD helps in designing better performing devices by combining mechanical and chemical engineering, resulting in the use of fewer resources. [PERSON] [9] tested various flap types in a wind tunnel, with the slotted flap showing one of the largest increases in \(C_{L}\). [PERSON] [10] and [PERSON] et al. [11] found increased \(C_{L}\) and \(C_{D}\) values in simulations by modifying airfoils with a slotted flap. [PERSON] and [PERSON] [12] found good agreement in \(C_{L}\) between their 3D simulation of a plain NACA 2412 and wind tunnel data from [PERSON] [13]; they did not compare their 3D model to a 2D one. [PERSON] and [PERSON] [14] investigated 2D and 3D models with varying wings spans of a NACA 0012; however, they did not include viscosity effects and considered the air as incompressible.
## 2 Method
### Singular Value Decomposition Filter for CPwc  
For one plane-wave image, we assume that there are \(n_{x}*n_{z}\) samples, where \(n_{x}\) and \(n_{z}\) is the number of samples along \(x\)-axis and \(z\)-axis. For a CPWC, there are \(n_{x}*n_{z}*N\) samples. We defined \(N_{s}=n_{x}*n_{z}\). The 3D dataset should be reshaped into a 2D matrix \(\mathbf{A}\) with dimension \((N_{s},N)\) to perform the SVD. The matrix \(\mathbf{A}\) is decomposed into a diagonal matrix \(\mathbf{\Sigma}\) with dimension \((N_{s},N)\), a unitary matrix \(\mathbf{U}\) with dimension \((N_{s},N_{s})\) of singular vectors related to the variations of the spatial sampling values and a unitary matrix \(\mathbf{V}\) with dimension \((N,N)\) of singular vectors related to the angular variations. The SVD of the matrix \(\mathbf{A}\) is defined as [31]:  
\[\mathbf{A}=\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{H}, \tag{2}\]  
where the diagonal matrix \(\mathbf{\Sigma}\) contains singular values \(\lambda_{i}(\lambda_{1}>\lambda_{2}>\lambda_{3}>\cdots>\lambda_{N})\) on the diagonal and \((.)^{H}\) denotes the conjugate transpose.  
It is assumed that the matrix \(\mathbf{A}\) contains effective signals and noise. Singular values can reflect the concentration of signals and noise energy. A larger singular value mainly reflects the effective signals and mainlobes, and a smaller singular value mainly reflects the noise and sidelobes [32]. Setting the singular values that reflect the noise to zero can reduce the noise and sidelobes in the signals and improve the signal-to-noise ratio. After the SVD processing, the matrix of the decomposed image data \(\mathbf{A}_{SVD}\) with dimension \((N_{s},N)\) is defined as:  
\[\mathbf{A}_{SVD}=\mathbf{U}\mathbf{\Sigma}_{\beta}\mathbf{V}^{H}, \tag{3}\]  
where \(\beta\) is a subscript threshold. \(\mathbf{\Sigma}_{\beta}\) is a diagonal matrix, which is obtained by setting singular values \(\lambda_{i}\) with \(i\) larger than \(\beta\) at zero in the matrix \(\mathbf{\Sigma}\). Then, the spatio-angular signals for point \(\mathbf{p}\) can be expressed as:  
\[s_{SVD}(x_{p},z_{p},\theta_{n})=\mathbf{A}_{SVD}[i(x_{p},z_{p}),j(\theta_{n})], \tag{4}\]  
where \(\mathbf{A}_{SVD}[i,j]\) denotes the element at the \(i\)-th row and \(j\)-th column of the matrix \(\mathbf{A}_{SVD}\). THe row index \(i(x_{p},z_{p})\) corresponds to the point \(\mathbf{p}=(x_{p},z_{p})\), while \(j(\theta_{n})\) is the column index corresponding to the \(n\)-th angle.  
Then, the CPWC output at point \(\mathbf{p}=(x_{p},z_{p})\) after SVD filtering can be obtained by:  
\[S_{CPWC+SVD}(x_{p},z_{p})=\frac{1}{N}\sum_{n=1}^{N}s_{SVD}(x_{p},z_{p},\theta_ {n}). \tag{5}\]
## 3 Methods and Results
### Feature Analysis between Refuters and Stiflers
Similarly, for the societal category, the VISm of the refuters was significantly lower than that of stiflers, while the refuters had higher SSm values. In contrast, in the political category, the VISm and LSm of refuters were significantly higher than those of the stiflers. The average values for the MSe (1-11) and the MSm (1-11) for the refuters and the stiflers in the 5 main categories were calculated and denoted \(\overline{\text{MSE}}\) and \(\overline{\text{MSm}}\). As shown in Table 4, there were no significant differences found between the refuters and the stiflers for the \(\overline{\text{MSE}}\) at a 95% confidence level, except for the economic category (the \(\overline{\text{MSE}}\) of refuters was significantly lower than that of stiflers). The \(\overline{\text{MSm}}\) of the refuters in all five categories, however, was higher than that of the stiflers, which indicated that the average short text refuter similarity degrees with the original rumor refuting microblogs were significantly higher than those of the stiflers. According to Table 5, at the 95% confidence level, in the disaster, economic and society related rumor refuting microblogs, correlations were confirmed between user behavior (refute/stifle) and user location (whether in the same province in which the rumor-related event occurred); however, in the political category, no correlations were found. As shown in Table 6, for users in the same location as the rumor refuting microblogs, the refuters were found to be less likely to retweet disaster, economic or society related rumor refutation information, with only 21.56%, 15.67%, and 23.77% of total viewers in the same province. One possible explanation is that these refuters know better about the local situation and do not feel the urge to spread truths. Therefore, the social media platform can recommend disaster, economic, or society related anti-rumor information to users not in the same location as the rumor refuting microblogs.
## 2 Materials and Methods
### Time Series of Vegetation Indices  
The vegetation indices are highly correlated with the biomass of vegetation, and their temporal changes represent the growth pattern of crops [54]. NIR is the spectral region most responsive to changes in vegetation density [42]. [PERSON] et al. (1974) [55] and [PERSON] (1979) [56] developed the normalized difference vegetation index (NDVI), which is based on NIR and red bands and is widely used. The soil-adjusted vegetation index (SAVI) [57], modified soil-adjusted vegetation index (MSAVI) [58], and enhanced vegetation index (EVI) [59] were all developed on the basis of NDVI. However, the usefulness of evaluating the vegetation temporal change pattern by using indices based on the visible bands has also been demonstrated [60]. In Planet Dove satellite images with a split-frame strip, observation times differ between RGB and NIR bands, which may result in misregistration (see above). Thus, it is also necessary to consider vegetation indices that use visible wavelengths only. These include the visible atmospherically resistant index (VARI) [17], green-red ratio vegetation index (GRVI) [61; 62], and normalized green ratio (GR) [60]. We calculated the following seven vegetation indices from pre-processed Dove satellite images to create a vegetation index time series:  
\[NDVI=\left(\rho_{NIR}-\rho_{Rad}\right)/\left(\rho_{NIR}+\rho_{Rad}\right) \tag{1}\]  
\[SAVI=\left\{(1-0.5)\times\left(\rho_{NIR}-\rho_{Rad}\right)\right\}/\left(\rho _{NIR}+\rho_{Rad}+0.5\right) \tag{2}\]  
\[MSAVI=\left\{2\times\rho_{NIR}+1-\sqrt{\left(2\times\rho_{NIR}+1\right)^{2}-8 \times\left(\rho_{NIR}-\rho_{Rad}\right)}\right\}/2 \tag{3}\]  
\[EVI=2.5\times\left(\rho_{NIR}-\rho_{Rad}\right)/\left(\rho_{NIR}+6\times \rho_{Rad}-7.5\times\rho_{Blue}+1\right) \tag{4}\]  
\[VARI=\left(\rho_{Green}-\rho_{Rad}\right)/\left(\rho_{Green}+\rho_{Rad}-\rho_{Blue}\right) \tag{5}\]  
\[GRVI=\left(\rho_{Green}-\rho_{Rad}\right)/\left(\rho_{Green}+\rho_{Rad}\right) \tag{6}\]  
\[GR=\rho_{Green}/\left(\rho_{Rad}+\rho_{Green}+\rho_{Blue}\right) \tag{7}\]  
where \(\rho_{Blue}\), \(\rho_{Green}\), \(\rho_{Rad}\) and \(\rho_{NIR}\) are Planet Dove blue, green, red, and near-infrared band reflectance, respectively.  
To suppress atmospheric noise, we applied the best index slope extraction (BISE) algorithm [63] and refined the time series data of the vegetation indices. The search range was set to 40 days for 10-day composite data (the missing periods were linearly interpolated), considering the observation frequency and agricultural calendar in the target area. To deal with unequal observation intervals in the 10-day composite data, we used an integrated algorithm based on BISE and the maximum value interpolated (MVI) algorithm [64] to refine the vegetation index time series data on a daily basis.
## 6 Conclusions  
In this study, the localization performance when dynamically rendering virtual sources generated with a virtual artificial head (VAH) was evaluated. We investigated both the difference in performance in regard to real sources and the impact of head tracking. Two different VAHs were used to capture the room impulse responses for different three-dimensional source positions in an anechoic room. Spectral weights calculated for 185 head orientations of individual listeners were applied to the measured room impulse responses to result in individually synthesized BRIRs, which were convolved with the test signal (speech) and presented through headphones.  
The major conclusions of the study can be summarized as follows: (i) Even in the absence of visual cues, similar localization accuracies as with real sources could be achieved with the VAHs with respect to azimuth, externalization and number of front-back reversals. The vertical localization performance using virtual sources generated with the VAHs was not as convincing as the vertical localization performance using real sources. (ii) The localization experiments with and without head tracking confirmed the importance of dynamic presentation for the localization accuracy when listening to virtual sources generated with the VAHs or with a conventional artificial head, even if head movements were limited only to the forward directions. (iii) With dynamic presentation being artificially enabled for speech signals, the non-individual BRIRs of the conventional artificial head performedsimilarly to individually synthesized BRIRs. (iv) Regarding the fact that in practical applications, binaural recordings with conventional artificial heads cannot be presented dynamically, the possibility of presenting binaural signals recorded with the VAH dynamically is the major advantage of virtual artificial heads over conventional ones.  
Conceptualization, M.F., M.H., S.D. and M.B.; Methodology, M.F., M.H., S.D., S.v.d.P. and M.B.; Software, M.F. and M.B.; Validation, M.F., M.H., M.B.; Formal analysis, M.F.; Investigation, M.F.; Writing--Original draft preparation, M.F.; Writing--Review and Editing, M.F., M.H., S.D., S.v.d.P. and M.B.; Visualization, M.F.; Supervision, M.H., S.D., S.v.d.P., D.P. and M.B.; Project Administration, M.B.; Funding Acquisition, M.H., D.P. and M.B.; All authors have read and approved the submitted manuscript.  
This research was funded by Bundesministerium fur Bildung und Forschung under grant number 03 FH021 DX5.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of the Carl von Ossietzky University of Oldenburg (Drs. 1r72/2018).  
Informed consent was obtained from all subjects involved in the study.  
We would like to thank all our subjects for their voluntary participation in the listening tests.  
The authors declare no conflict of interest.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 1 Introduction  
Mobile ad hoc networks (MANETs) are becoming very popular because these networks can be set up easily and be operated on the go. These features allow MANETs to be used in wide applications in areas such as battlefields, disaster relief operations, and even in hotel lobbies, airports, and cafes [1]. Another feature of MANETs is that the mobile nodes can configure themselves without any centralized control. However, it is a challenge to maintain a high level of performance on the part of the MANETs due to the node mobility, battery-powered nodes, and multi-hop routing structure [2, 3, 4].  
Wired networks are faster and provide more security and reliability [5, 6, 7]. Faults are generally less common in wired networks. The difficulties with wired networks show mainly in the installation and overall costs to repair, maintain, and manage these setups. Unlike wired networks, wireless networks help to provide flexibility, as they allow userswithin the network to work from anywhere within the boundaries of the network itself [2; 8]. However, wireless networks are less reliable regarding the connections and security [9].  
Infrastructure-based networks are where many wireless devices are connected to the network infrastructure via an access point or router [10]. The access point allows users to connect to external networks like the Internet. Infrastructure-less or ad-hoc networks can start with a peer-to-peer connection between two wireless devices. As more ad hoc wireless devices enter this network by sharing the same SSID (service set identifier), this becomes a multi-node system of connections where many users can exchange data and communicate with one another [7]. Between the two categories, ad hoc networks are simpler and easier to implement, because no pre-existing setups and hardware are needed to establish a connection. And because of this, the overall costs to implement such a network are low.  
Ad hoc networks are categorized into wireless sensor networks (WSNs), wireless mesh networks (WMNs) and MANETs. A MANET is a network where the mobile nodes are in communication with one another without administration assistance [11; 12]. Instead of having an access point, a wireless device connected to the network can act as a centralized router with access point software. In these networks, routing is needed to find a suitable path from a source node to its destination with the help of routing protocols.
## 5 Discussion  
As a result of the experiments, the method based on detecting complex anomalies using Isolation Forest successfully identified laboratory-confirmed instances exceeding the MPC. In contrast, the benchmark approaches used in this study, despite their sensitivity, did not yield positive results. This is likely because exceeding the MPC in water contamination is indicated by significant changes across multiple data collection channels simultaneously. Therefore, the concept of detecting complex anomalies with this method is valid but requires careful analysis. Specifically, issues such as selecting the appropriate time window for sensor data and aligning time series with one another are areas that could be further developed in this study.  
It is important to note that the detected events exceeding the MPC that are not included in the initial data sample (for IFPC, this includes 57 different days, which represents 16% of the total number of days in the study period). These events can be categorized into the following groups:  
* Actual event of water contamination exceeding the MPC for which there is no laboratory test confirmation and, therefore, it was not included in the data sample.
* False positive response of the anomaly detection method, i.e., there was no actual excess.  
Without complete and reliable information, it is difficult to assess errors in the proposed approach using the same criteria as binary classification methods. Given iForest's ability to detect similar patterns in time series data and the stringent rules for identifying  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Dates of Laboratory Recorded Facts** & **Number of Detection** \\ \hline
3\(\sigma\)PC & 7 June 2022 & 0/3 \\ k-meansPC & 8 June 2022 & 0/3 \\ IFPC & 9 June 2022 & 3/3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results of the experiments.  
Figure 13: List of dates obtained by the IFPC method.  
complex anomalies through predicate conjunction, we can conclude the following: the identified contamination events exceeding the MPC are'similar' to confirmed cases, with a confidence level defined by the'reliability' indicator of the decision trees. This allows us to introduce a measure of how closely the identified results match confirmed cases based on these characteristics. To enhance this approach, it is important to classify contamination events according to specific substances (such as fluorine, iron, etc.). This will enable more accurate diagnostics when using the IFPC method for particular substances. Additionally, the conjunction of predicates can detect complex anomalies not only by analyzing events across different channels but also by examining events within the same time series across various observation intervals. In this context, a complex anomaly can be viewed as connections between sequences of events.