esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 7 Network, System, and Channel Model
### System Model  
Under the given network model and considering a multi-antenna setup, with each gNB equipped with M antennas and each UAV having a single antenna, the signal received at the UAV from the \(\mathcal{L}\)-th gNB can be expressed as  
\[y_{l}=c_{l,l}^{*}h_{l}x_{l}+\sum_{b\
eq l}c_{l,b}^{*}h_{b}x_{b}+n_{l} \tag{9}\]  
Here, \(x_{l}\), \(x_{b}\)\(e\)\(\mathbb{X}\) are transmitted signals from the \(\mathcal{L}\)th and \(\mathcal{B}\)th gNBs, and it meets the power constraint  
\[\mathbb{E}[|x_{l}|^{2}]=P_{\text{TX,l}} \tag{10}\]  
The vectors \(c_{l,l}\), \(c_{l,b}\), and \(M\times 1\) are channel vectors representing the connection between the UAV and the \(\mathcal{L}\)-th gNB, and the connection between the UAV and the \(\mathcal{B}\)-th gNB, respectively. Finally, \(n_{1}\) is drawn from a complex normal distribution with zero mean and variance of \(\sigma_{\text{n}}^{2}\), representing the noise received by the UAV.  
The first term in Equation (9) represents the desired signal received by the UAV, while the second term represents the interference received from non-associated gNBs.  
Each gNB \(\mathcal{L}\) transmits power \(P_{\text{TX,l}}\)\(e\)\(\mathcal{P}\) from the set of all possible power values, denoted by \(\mathcal{P}\). This set is defined such that the possible transmit power is a power offset.
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 7 Conclusions and Future works  
Despite the success of Vanilla NDN, security and privacy issues still need to be addressed. The intrinsic principle of security in NDN and its breeds such as VNDN have caused other issues such as the overhead incurred by hierarchical data authenticity. In principle, hierarchical data authenticity eliminates all types of man-in-the-middle attacks but at the expense of huge computational, storage, and delay overhead, mostly undesired in highly mobile and energy-restricted networks such as FANET. To fill the voids, we proposed a novel Trust-Aware Monitor-based Communication Architecture for Flying Named Data Networking. Our proposed scheme leverages a trust-aware Network Monitoring technique to avoid the broadcast storm problem during the interests dissemination. Our proposal uses the inter-UAV trust to choose whether to check the data authenticity for a particular node or not without affecting the desired security levels. Simulation results proved the efficiency of our solution at sustaining Vanilla NDN security levels together with a clear overhead and energy minimization. The paper also discussed main open challenges in FNDN security.  
As future work we plan to investigate mainly the data privacy and caching policies. In addition, we also plan to investigate the trustworthiness of the data provided by the heterogeneous vehicular networks involving different kinds of technologies and features.  
All authors contributed equally to this work. This research is partially supported by the United Arab Emirates University (UAEU) under grant number 31T065. The authors declare no conflict of interest.
## 5 Results Evaluation and Discussion
### Simulation Parameters  
The simulation parameters used for the implementation of CSFSA is shown in Table 3. The more chosen for simulation is Wismote which is MSP430 microcontroller based board having 16 KB of RAM and 256 KB of flash memory and a radio chip CC2420 compatible with IEEE 802.15.4 standard. The protocols used in the simulation are CoAP as an application layer protocol, UDP as transport layer protocol, 6 LoWPAN as network layer protocol and IEEE 802.15.4 as link layer protocol.
## 8 Issues and Recommendations for Future Research
### Intelligent Distributed Computing and Autonomous Networking  
An important aspect of intelligent IoV is the training and deployment of deep learning models. It may be too difficult to model and run computationally intensive deep learning models directly on vehicles. Directly moving data to a central server for training, e.g., at a cloud server, will introduce prohibitive communication overheads. Offloading them to the edge server may cause impairments due to time-varying wireless fading channels and lead to excessive latency when the offloaded data size is large. Besides enjoying the general benefits of edge intelligence, various challenges are presented, such as meeting the QoS requirements of applications, including real time requirements, such as 20-100 ms latency in safety application, high level accuracy and reliability for autonomous vehicles in 3D object detection and inference. Various techniques have been proposed to improve the performance of edge intelligence to enable inferences in IoV networks. Model compression is one enabling technique to alleviate the issue of resource-intensive deep learning models and resource-constrained edge devices. Model partition is another technique to alleviate the burden of executing deep learning models on vehicles, offloading the computational intensive tasks to the edge node (RSU) or other vehicles to obtain models to perform inference. Model partitions can be between vehicle and edge nodes or among vehicles for vehicle-edge node collaboration, according to the current system environment factors, such as network bandwidth, device resource and edge-server load.  
Autonomous networking design is important to achieve efficient edge intelligence service provisioning under dynamic heterogeneous wireless network coexistence (e.g., LTE/5G/Wi-Fi/Bluetooth), allowing newly added edge devices to self-configure in a plug-and-play manner. Furthermore, ultra-reliable low-latency communication (URLLC) has been defined in 5G new radio for mission-critical application scenarios that demand low delay and high reliability. Thus, it will be valuable to integrate 5G URLLC capability with edge computing to provide ultra-reliable and low-latency edge services.
## 3 Data and Data Processing
### InSAR Data Processing  
Both datasets were processed using a two-step procedure. First, interferograms were generated using criteria as described below (Section 3.3), and then InSAR time series analyses were calculated using the SBAS technique to obtain velocity values. For Sentinel-1 data, we used the ROI_PAC software [42] to generate interferograms and The Miami INsar Time-series software in PYthon (MintPy) package [43] to calculate the time series. For COSMO-SkyMed data, we used the ENVI SARscape 5.5 (Exelis Visual Information Solutions, Boulder, CO, USA) software to generate interferograms and to calculate time series products.  
All interferograms were calculated using the SRTM Global 1 arc second dataset [44; 45] in order to remove the topographic component from the phase component. In addition, the multi-looking process was applied to all DInSAR results to reduce phase noise in both range and azimuth directions. The resulting ground resolution was 20 m for the COSMO-SkyMed and 100 m for the Sentinel-1 datasets. We also performed interferogram unwrapping and filtering to improve the signal-to-noise ratio [46]. Temporal coherence is considered as univariate exponential function of time, calculated by taking the random motion of scatterers in a resolution cell [47]. It ranges from 0, where there is no useful information, to 1, where there is no noise. A threshold for temporal coherence is used to select SAR pixels with reliable results. Therefore, in order to extract decorrelated pixels from SBAS results derived from COSMO-SkyMed and Sentinel-1 data, we selected only SAR pixels that exhibited a temporal coherence value larger than 0.7.
# Trepenoid Biosynthesis Dominates among Secondary Metabolite Clusters in _Mucoromycotina_ Genomes
## 3 Results and Discussion
### Taxonomic Distribution  
Genes similar to known secondary metabolite clusters were identified in the majority of analysed (\(n=148\) out of 157) _Mucoromycota_ genomes (see Supplementary Table S1, Figure 2). Genomes of _Mortierellomycin_ encode an average of 16 SMCs per genome; the fewest were found in _Lobosporangium transversale_ NRRL 3116 (\(n=5\)) and _Mortierellales_ strain BCC40632 (\(n=3\)). Interestingly _Glomeromycotina_ genomes differed in the number of SMCs, with half of _Glomerales_ isolated devoid of SMCs but the _Diversisporales_ having more than 20 SMCs. However, all seven _Glomerales_ isolates without detected SMCs lacked gene annotation. _Mucoromycotina_ genomes constituted the majority of the analysed dataset and, as expected, displayed intermediate content of SMCs, ranging from 22 SMCs in _Rhizopus azygosporus_ CBS 357.93 to no SMCs candidates in _Cunningham bertholletiae_. Taxa with multiple sequenced strains revealed strong intraspecies diversity; for example, _Rhizopus oryzae_ strains had from 2 up to 11 SMC candidates. This could be either due to assembly quality issues because SMCs tend to accumulate in repeat-rich regions, which are often missing from assemblies, or to real genetic diversity within the species.  
Detection of SMCs also depends on the gene calling method and evolutionary distance to the reference genome used for training the gene calling tool. As mentioned before, this was particularly pronounced for _Glomeromycotina_ genomes, wh  
Figure 2: Schematic summary of the taxonomic distribution of particular secondary metabolite cluster (SMC) types. Filled rectangles, clusters of this type are present in more than 50% of isolates of the taxon; empty shape, in less than 50% of isolates; no shape, no clusters.  
when the built-in GlimmerHMM (fungal version) was used. However, several clusters belonging to different types were reported when a GFF annotation was supplied. In consequence, we used genomic annotations when available.
###### Abstract  
The turbulence effect, thermal blooming effect, laser beam aberration, platform jitter, and other effects in the process of high energy laser propagation in the atmosphere will cause serious degradation of laser beam quality, which will have a negative impact on the actual application of laser propagation engineering. It is important in the engineering application of high-energy laser propagation to evaluate the far-field intensity distribution quickly. Based on the optical transfer function (OTF) theory of imaging system, the propagation process of high-energy lasers is modeled as the imaging process of point source. By using the convolution of point spread function (PSF) of jitter, turbulence, thermal blooming, and aberration of emission system, fast calculation of the far-field intensity distribution of high energy laser is realized. The calculation results are compared with those obtained by the 4D wave optics simulation program in different propagation scenarios. The results show that the calculated facula distribution and encircled energy of this method are in good agreement with the simulation results of wave optics, which can realize the fast and accurate evaluation of the far-field intensity distribution of high-energy laser propagation and provide a reference for practical engineering application.  
atmospheric optics; high energy laser; turbulence; thermal blooming; convolution +
Footnote †: journal: Applied Sciences  
11 March 2021  
10 May 2021
# A Vehicle Velocity Prediction Method with Kinematic Segment Recognition
## 2 Data Preprocessing
### Principal Component Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline \
\begin{tabular}{c} **Principal** \\ **Component** \\ \end{tabular} & \multirow{2}{*}{**Eigenvalue**} &
\begin{tabular}{c} **Contribution** \\ **Rate (\%)** \\ \end{tabular} & \multicolumn{1}{c}{**Cumulative**} \\  & & **Rate (\%)** & **Contribution Rate (\%)** \\ \hline
1 & 4.5073 & 45.0372 & 45.0732 \\
2 & 1.8052 & 18.0520 & 63.1251 \\
3 & 1.5257 & 15.2566 & 78.3817 \\
4 & 0.6908 & 6.9083 & 85.2901 \\
5 & 0.5545 & 5.5446 & 90.8346 \\
6 & 0.4695 & 4.6949 & 95.5295 \\
7 & 0.2602 & 2.6018 & 98.1312 \\
8 & 0.1190 & 1.1901 & 99.3213 \\
9 & 0.0586 & 0.5859 & 99.9072 \\
10 & 0.0093 & 0.0928 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics and contribution rates of the principal components.
# LEO-Augmented GNSS Based on Communication Navigation Integrated Signal
## 3 Performance of Communication Signal
### BER Performance
When \(CNSR\) is above 15, \(CN0_{total}\) should be increased less than 1 dB to maintain the BER performance. According to Section 2.3, \(CNSR\) should be as low as possible so that \(CN0_{eff}\) of the navigation signal is high. According to the BER performance, \(CNSR\) should be as high as possible. Therefore, the best choice for \(CNSR\) when the bandwidth of the navigation signal is 10.5 MHz should be the balance of the communication performance and navigation performance. Based on the analysis above, 15 dB is the optimal choice for \(CNSR\). Figure 7: Bit error rate (BER) under versus \(CN0_{total}\) under different \(CNSR\).
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 4 Experimental Setup
### Receiver and Algorithm Implementation
#### 4.1.2 LBCA-Based DSKF and LUT-DSKF Configuration  
The selected weighting function \(g[n,B_{N}]\) is a linear combination of two Sigmoid functions and has the following expression:  
\[g[n,B_{N}]=\begin{bmatrix}0.014\\ 0.086\end{bmatrix}^{T}\begin{bmatrix}\text{Sig}(50(B_{N}-0.06))\\ \text{Sig}(250(B_{N}-0.36))\end{bmatrix} \tag{48}\]  
where \(\text{Sig}(\cdot)\) is the Sigmoid function [44]. Figure 12 shows the graphical representation of Equation (48). \(T_{\text{LBCA}}\) is the normalized dynamic threshold and indicates the sensitivity of the algorithm to signal dynamics. In this case, \(g_{\text{Max}}\) is set to 0.1 and \(T_{\text{LBCA}}\) to 0.14. This weighting function presented the best results in the LBCA-based PLL [7].  
Figure 11: Photo of the GOOSE receiver @Fraunhofer IIS/[PERSON].  
Figure 12: Selected Normalized weighting function in LBCA-based techniques.  
In the LBCA-based DSKF, \(R\) is set to \(10^{-7}\) cycles2. From Figures 6 and 12, one can observe that \(q\) varies between \(10^{-3}\) cycles\({}^{2}\)/s\({}^{6}\) and \(10\) cycles\({}^{2}\)/s\({}^{6}\), considering \(\tau_{\text{int}}=~{}20\) ms.
## 3 Results and Discussion
### Potential Replication of Biomethane Production from WWTP  
The results of the Smart Met Value project were very positive. A traditional WWTP was transformed into a gas plant capable of supplying biomethane to vehicles in the city of Guadalajara (Spain) [20]. In this city, with more than 93,000 inhabitants and a WWTP capable of treating 45,000 m\({}^{3}\) of sewage per day, 285 kg of biomethane per day have been produced. Considering that a waste collection truck consumes 27.22 kg of natural gas per 100 km travelled, biomethane production could be sufficient to supply a fleet of seven trucks travelling 150 km per day.  
These results demonstrate that the transformation of sewage sludge into biofuel is not only feasible but can also be applied in any WWTP in the world, allowing cities to reduce their dependence on fossil fuels and improve their energy efficiency in a sustainable manner.  
Based on the data obtained in the project, the replication potential of the project has been estimated on a national scale, as explained in Section 2.1.2. It is estimated that Spain has a total potential of 1.026 TWh per year of biomethane potential from WWTP, with  
Figure 4: Potential biomethane production from pig manure per province in GWh/year.  
significant concentration in the main metropolitan areas. Figure 5 shows the estimated potential biomethane production of WWTPs by province, and a detailed table with the data by province is included in Appendix B. The largest cities, such as Madrid (139.1 GWh/year), Barcelona (139.08 GWh/year), and Valencia (91.77 GWh/year), concentrate most of this potential due to the high volume of treated sewage, in contrast to less populated provinces such as Teruel or Soria. These results are in line with the estimates given in the Sedigas report related to the potential biomethane production in Spain [24].
# A Harmonized Information Security Taxonomy for Cyber Physical Systems
###### Abstract  
Cyber physical systems (CPSs) are found in many aspects of daily life, and they control and protect energy production, manufacturing and even healthcare. Due to long lifecycles and the use of legacy technologies, its associated security comes with many challenges. Security taxonomies are useful to classify and communicate security-related information and elements. Despite the existence of numerous taxonomies, they are fragmentary, limited to only specific lifecycle phases or cover only specific aspects. A harmonized taxonomy must be applicable to all lifecycle phases of the CPS. This paper presents well-established taxonomies that are combined into a single comprehensive and harmonized taxonomy and allows application throughout the different lifecycle phases. Application of the taxonomy to real-world scenarios requires a consistent implementation methodology. The use of the harmonized taxonomy methodology is demonstrated by applying it to an actual incident case study. The taxonomy is used to identify information security gaps through its implementation in the industrial facility in question. The identified gaps are then addressed as part of the security lifecycle of the CPS. The harmonized taxonomy can be expanded to apply it to industries with specific requirements.  
cyber physical systems; risk management; taxonomy; information security; industrial control and automation +
Footnote †: journal: Applied Sciences  
1  
Footnote 12: email  
management. Furthermore, this relative placing allows for specific information security implementation and incident management aspects to be uniquely categorized. Because of this, taxonomies form an important part of information management frameworks [7].  
A comprehensive and consistent information security management framework for CPSs that addresses the full lifecycle--without unnecessary complication--is not available. Taxonomies related to information security tend to address limited aspects of security management. More comprehensive taxonomies for CPSs [8] have been proposed, but these taxonomies still concentrate on specific aspects, like incidents, and are not applicable throughout the system's lifecycle. The main goal of the current paper is to address the fragmented coverage of this dilemma by providing a broad, harmonized taxonomy for CPSs. This is accomplished by investigating the existing taxonomies in both the information and operational technology fields and combining them into a cohesive whole.  
The rest of this paper is constructed as follows. The background section analyzes the leading standards and considers the typical lifecycles described by these standards. This is followed by a discussion of the established taxonomies. In the next section, the taxonomies are grouped into core elements, followed by a section that provides the harmonized taxonomy. The latter is validated by mapping a well-known case study to the taxonomy in the penultimate section, which is followed by the conclusion of the paper.
## 3 Materials and Methods
### Data Attributes  
In addition to addressing specific research questions (detailed in next section), this review first takes a broader approach by providing bibliometric and scoping insights to contextualize the literature landscape. Key data attributes were collected from each selected paper; these were as follows:  
* Number of yearly publications by source type: Tracks the volume and source types (e.g., journals, conferences) of publications over time, reflecting trends and shifts in scholarly attention toward resilience enhancement in risk assessment.
* CI sectors addressed: Identifies the focus sectors (e.g., energy, transportation) targeted by each paper, providing insight into which CI areas are prioritized in resilience-related studies.
* Author keywords: Lists the most frequently occurring keywords, indicating prominent themes within the literature.
* Inter-article connectivity via shared topics: Examines the network of articles interconnected by shared topics, highlighting collaborative patterns and thematic linkages across studies.  
These attributes offer a comprehensive overview of the research landscape, helping to identify significant patterns, emerging topics, and potential gaps in the resilience-related risk assessment literature (reported in Section 4.1), which provides foundational knowledge before a full paper analysis aimed at answering the research questions derived from the literature.
## 1 Introduction  
Epilepsy is a neurological disorder characterized by recurrent episodes of signs and symptoms caused by abnormal brain synchronization and fast neuronal activities. Excessive electrical activity in brain cells manifests as epileptic seizures, one of the major neurological chronic illnesses affecting 50 million people globally [1]. Epileptic seizures can lead to serious cognitive, neurological, and physiological implications, including loss of consciousness and even death if thorough diagnosis and monitoring are not conducted [2, 3]. Neurologists employ clinical EEG (electroencephalogram) to detect epileptic seizures. The EEG signals are captured by placing multiple electrodes on different areas of the patient's scalp. The biomedical signals recorded using EEG are difficult to analyze with the naked eye. Continuously analyzing EEG signals is a significant challenge since it always requires the presence of a neurophysiologist in case long-term monitoring is required [4]. Furthermore, accurate identification of seizures is time-consuming and requires expert knowledge. To address these challenges, various machine learning methods are used to automate the analysis of EEG data. The machine learning model is trained using the characterizing properties extracted from the EEG signal for automatic detection/classification of epileptic seizures.  
The aim of this study is to employ a random neural network (RNN)-based approach to develop a novel EEG classification scheme to detect epileptic seizures. To achieve this,data are acquired from two publicly available datasets that have been extensively used in the literature for training and testing machine learning models. The datasets contain annotated single-channel and multi-channel EEG recordings acquired from patients with epilepsy. Below are the main contributions of this research:  
* A novel AI-based machine learning model is presented for the classification of epileptic seizures using RNN.
* The proposed model is developed by running multiple RNN-based experiments using different parameters such as training and testing ratio; learning rate (LR); and frequency analysis techniques, i.e., discrete wavelet transform (DWT) and fast Fourier transform (FFT).
* A comparison of the novel model is presented with traditional classification algorithms such as ANN and SVM.
* A critical evaluation of the obtained results concludes that the proposed RNN-based scheme is the most efficient in terms of accuracy, sensitivity, precision, and specificity for the classification of epileptic seizures.