esa-sceva/satcom-chunk-collection huggingface dataset:
# Poisoning Attacks against Communication and Computing Task Classification and Detection Techniques
## 8 Conclusions  
Throughout this article, a hybrid _k-means/k\({}^{\prime}\)-NN_ technique was presented to assist with near-optimal 2C task classification in edge computing for 5G and beyond communication systems. The technique outperformed the classical round robin scheduling technique in terms of the processing time of the 2C tasks, the fulfillment of constraints for the assigned link server to each task, and the number of used servers. After that, two attack types, named _attack1_ and _attack2_, were proposed to weaken the performance of the ML method. Both methods showed down-degradation in the ML technique. Higher unaccepted 2C tasks regarding the evaluation constraints were observed, and longer processing times  
Figure 11: Method 2 results.  
were observed under the presence of these attacks. The later attack successfully launched cluster poisoning, where the ML classification scheme changed completely and the servers became overloaded by the examples assigned to other omitted clusters. Finally, to mitigate these attacks, two anomaly detection methods were proposed. Each one was devoted to a single attack type due to the limitations they faced. To mitigate _attack1_, more power was needed where the end server had higher computational capability, but this ensured the minimum QoS needed to serve the tasks under this attack. For _attack2_, extra statistical tools were used. Both mitigation techniques showed good detection and recovery of the classification system, where the processing time recovered to near the optimal scenario. In addition, lower unaccepted 2C requests in terms of evaluation constraints were observed when applying these solutions.  
For future considerations, a complete system will be studied, and link servers with higher diversity will be addressed. In addition, the propagation conditions in both the radio and wired media will be studied. A full real environment with real network characteristics will be modeled. Hence, the impact of poisoning attacks in real-world scenarios will be covered. Moreover, how the channel can affect the poisons and how the attacker has to be aware of the channel conditions will be analyzed. Furthermore, the ML technique has to be more optimized and compared to the state-of-the-art techniques. The exploration phase of attacks will be analyzed as well.  
Conceptualization, H.B.; methodology, H.B.; software, Y.S.; validation, Y.S.; formal analysis, Y.S.; investigation, Y.S.; resources, H.B.; data curation, Y.S.; writing--original draft preparation, Y.S.; writing--review and editing, H.B.; visualization, Y.S.; supervision, H.B.; project administration, H.B.; funding acquisition, H.B. All authors have read and agreed to the published version of the manuscript.  
The presented work was funded by the Polish Ministry of Education and Science within the research bailout in 2022 and 2023.  
Data Availability Statement: Data is unavailable due to privacy.  
The authors declare no conflicts of interest.
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 8 Conclusions  
In this paper, we presented an overview on CR-based UAVs, as well as their hardware and software requirements. We also indicated the first steps taken towards integrating CR with UAVs, and a review of the state-of-the-art of CR for UAV communications. This work has also shown that several questions remain unclear, in particular regarding the impact of CR to UAVs energy consumption, and the effects of UAVs mobility to CR spectrum mobility and spectrum sharing. We also specified steps to build a simple and low-cost CR-based UAV testbed.  
Future work shall consider a channel occupation mapping aiming at achieving an UAV flight path guided by spectrum occupancy. PU arrivals prediction based on Machine Learning algorithms on real data collected by UAVs is a key element concerning the study of CR algorithms for UAVs. Finally, CR hardware and software that meet the constraints and requirements of UAVs are also needed.  
Papers compilation, state-of-the-art review, and CR-based UAV testbed development and deployment, G.M.D.S.; analysis of software characteristics and AI/Machine Learning methods applied to CR, R.S.d.C.; supervision, K.R.L.J.C.B. All authors have read and agreed to the published version of the manuscript.  
This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001. Research was also sponsored by the Army Research Office and was accomplished under Grant Number W911 NF-18-1-0012. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.  
The data collected by the CR-based UAV testbed are available online: [[https://github.com/GMDSantana/pub-files/tree/main/sensors-2021](https://github.com/GMDSantana/pub-files/tree/main/sensors-2021)]([https://github.com/GMDSantana/pub-files/tree/main/sensors-2021](https://github.com/GMDSantana/pub-files/tree/main/sensors-2021)).  
The authors declare no conflict of interest.  
Figure 13: Amount of 6 MHz TV channels available for SUs in the United States in 2015. Adapted from the work in [90].
## 3 Oscillation Analysis of FSC-VSPSU
### Equivalent Impedance Parameters of the Grid Network  
When changing the equivalent inductance of the grid network (\(L_{\mathrm{g}}\)), the relevant Bode diagram of the impedance matching analysis is shown in Figure 14. As it can be seen, when \(L_{\mathrm{g}}\) is set to 0.8 mH, the phase margin is negative, which indicates that the system may experience instability. Furthermore, when \(L_{\mathrm{g}}\) is decreased to 0.6 mH, the phase margin is slightly increased to \(5^{\circ}\), and the weak phase margin indicates that the system will experience oscillation. When \(L_{\mathrm{g}}\) is further decreased to 0.4 mH, the phase margin is enhanced, and the oscillation will be weakened. At last, when \(L_{\mathrm{g}}\) is further decreased to 0.1 mH, the impedance amplitude curves have no intersection, and so the sufficient phase margin makes the system extremely stable.  
When changing the equivalent resistance of the grid network (\(R_{\mathrm{g}}\)), the relevant Bode diagram of the impedance matching analysis is shown in Figure 15. As it can be seen, when \(R_{\mathrm{g}}\) is appropriately increased, the intersections of the impedance amplitude curves almost have no movements, and the corresponding phase margin is improved, which can suppress the oscillation phenomenon that occurs in the system.  
Figure 14: Bode diagram when the equivalent inductance of the grid network changes.  
Figure 15: Bode diagram when the equivalent resistance of the grid network changes.
# Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning
## 3 Materials and Methods  
Given the state of the research field and the importance of automating pavement detection systems, to allow for effective condition monitoring, the most integral part of the work was the development of the object detection model which was done within the open-source TensorFlow environment [62]. The setup was done to ensure compatibility with this environment. The workflow to do this is shown in Figure 3, wherein the steps are shown from data collection to final model deployment. This workflow is further explained in Sections 3.1-3.5. It is fully replicable for other datasets and can be utilized for generating models for different cities or regions to enable creating a model based on particular conditions that exist within the road authorities' environment.  
Figure 3: Pipeline utilized for the development of object detection model.
# NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
## 3 The NSGA-PINN Framework
### Non-Dominated Sorting  
The proposed NSGA-PINN utilizes non-dominated sorting (see Algorithm 1 for more detailed information) during PINN training. The input \(P\) can consist of multiple objective functions, or loss functions, depending on the problem setting. For a simple ODE problem, these objective functions may include a residual loss function, an initial loss function, and a data loss function (if experimental data are available and we are tackling an inverse problem). Similarly, for a PDE problem, the objective functions may include a residual loss function, a boundary loss function, and a data loss function.  
In the EAs, the solutions refer to the elements in the parent population. We randomly choose two solutions in the parent population \(p\) and \(q\); if \(p\) has a lower loss value than \(q\) in all the objective functions, we define \(p\) as dominating \(q\). If \(p\) has at least one loss value lower than q, and all others are equal, the previous definition also applies. For each \(p\) element in the parent population, we calculate two entities: (1) domination count \(n_{p}\), which represents the number of solutions that dominate solution \(p\), and (2) \(S_{p}\), the set of solutions that solution \(p\) dominates. Solutions with a domination count of \(n_{p}=0\) are considered to be in the first front. We then look at \(S_{p}\) and, for each solution in it, decrease their domination count by 1. The solutions with a domination count of 0 are considered to be in the second front. By performing the non-dominated sorting algorithm, we obtain the front value for each solution [21].  
```
inputs:\(\mathrm{P}\); for\(p\in P\)do  n = [] ; // set of points be dominated by other points \(\mathrm{S}\) = [] ; // set of points dominate other points for\(q\in P\)do if\(p<q\)then \(S_{p}=S_{p}\cup\{q\}\) ; // q is dominated by p elseif\(q<p\)then \(n_{p}=n_{p}+1\) if\(n_{p}=0\)then \(p_{rank}=1\); \(F_{1}=F_{1}\cup\{p\}\) ; // assign p to the first front  i = 1; while\(F_{i}\
eq\emptyset\)do \(Q=\emptyset\) ; // used to store the members of the next front for\(p\in F_{i}\)do for\(q\in S_{p}\)do \(n_{q}=n_{q}-1\); if\(n_{q}=0\)then \(q_{rank}=i+1\); \(Q=Q\cup\{q\}\) ; // q is in the next front  i =i+1; \(F_{i}=Q\);
```  
**Algorithm 1**Non-dominated sorting.
## 4 Experiment
### Comparison Method  
To validate that the proposed model outperforms other models in predictive performance, the SSP-AA model proposed in this paper is compared with four types of models: heuristic, Graph Auto-Encoder (GAE), Latent Feature-Based (LFB), and SGRL models. For heuristic models, we used Common Neighbors (CNs) [44] and Adam Adar (AA) [45]. The GAE includes GCN [46] and GIN [47] encoders and employs the Hadamard product of a pair of node embeddings as the decoder. LFB models consist of matrix factorization (MF) [48] and node2 vec (n2v) [49], supplemented with a logistic classifier head. We selected the advanced Sampling Enclosing Subgraphs (ScaLed) [50] model from SGRL for comparison with the SSP-AA model. Detailed introductions to each of the models are as follows:  
* CN: Evaluates the similarity between two nodes by calculating the number of common neighbors between them. Pairs of nodes with more common neighbors are more likely to form connections in the graph;
* AA: Utilizes the concept of common neighbors to assign weights to neighbors, weighted according to the degree of the neighbor nodes. Common neighbors with lower degrees are assigned higher weights, as they could potentially be more predictive features;
* GCN: Learns the representations of nodes in the graph by performing convolution operations on the features of the node and its neighbors, thereby capturing the local structural information in the graph;
* GIN: Employs an iterative message-passing mechanism to update node representations by aggregating information from neighbor nodes, aiming to capture the global structural information of the graph;
* MF: Discovers latent node features by decomposing the adjacency matrix; these latent features can capture implicit relationships between nodes and thus can be used for predicting future connections;
* n2v: Generates node sequences by performing random walks in the graph. These sequences are used as input to train a skip-gram model, thus learning the vector representations of nodes in the graph, which can be used for link prediction tasks;
* ScaLed: By aggregating information from neighbor nodes in the graph, this method can capture the local structure of the graph and use this information to predict future connections.
# Hybrid Modeling for Stream Flow Estimation: Integrating Machine Learning and Federated Learning
###### Abstract  
In the face of mounting global challenges stemming from population growth and climate fluctuations, the sustainable management of water resources emerges as a paramount concern. This scientific endeavor casts its gaze upon the Upper Euphrates basin, homing in on the Tunceli Munzur water sub-basin and the Sakarya Basin's Kutahya Porsuk Stream Besdegirmen rivers. The investigation unfolds through the intricate analysis of daily average flow data, total daily precipitation, and daily average air temperature values, with the objective of unraveling the complexities of future water potential estimation. Central to our exploration are a series of well-established techniques including linear regression (LR), support vector regression (SVR), decision tree (DT), random forest (RF), and extra trees regression (ETR). We employ these methodologies slightly to decipher patterns woven within the dataset, fostering an informed understanding of water dynamics. To ascend the pinnacle of estimation accuracy, we introduce a groundbreaking hybrid approach, wherein the enigmatic wavelet transform (WT) technique assumes a pivotal role. Through systematic stratification of our dataset into training, validation, and test sets, comprising roughly 65%, 15%, and 20% of the data, respectively, a comprehensive experiment takes shape. Our results unveil the formidable performance of the ETR method, achieving a striking 88% estimation accuracy for the Porsuk Stream Besdegirmen, while the RF method garners a commendable 85.2% success rate for the Munzur water Melekbahee. The apex of innovation unfolds within our hybrid model, a harmonious fusion of methodologies that transcends their individual capacities. This composite entity elevates estimation success rates by a remarkable 20% for the Munzur water Melekbahee and an appreciable 11% for the Porsuk Stream Besdegirmen. This amalgamation culminates in an extraordinary overall success rate of 97.7%. Our findings transcend mere insights, resonating as guiding beacons for navigating the intricate maze of water resource management in an era marked by uncertainties. This study underscores the indispensability of advanced mathematical paradigms and machine learning frontiers, fortifying the bedrock of sustainable water resource management for the generations to come. By harnessing the fusion of federated learning and a constellation of innovative techniques, we endeavor to illuminate the path towards deciphering the complex tapestry of water resource estimation and management, facilitating a resilient and enduring aquatic world.  
flow estimation; temperature; precipitation; regression; wavelet transform +
Footnote †: journal: Medical Imaging  
1
# Analysis of Road Sign-Related Factors Affecting Driving Safety with Respect to City Size
## 5 Discussion
### Analysis with Respect to City Size  
The dependence of the aforementioned conclusions on city size is as follows: First, the variable of daytime road sign recognition (odds ratio: 1.443-1.735) was observed to be significant for all city sizes, indicating the need to promote road sign improvement regionally. This can be achieved by improving designs to enhance intuitiveness, avoid excessive information, and select necessary information based on user requirements. Second, the variable of customized guidance for various forms of mobility (1.363) exhibited statistical significance only in Seoul. Owing to its high population density, Seoul's roads feature a mixture of traditional vehicles and various other forms of mobility, e.g., personal mobility and bicycles, thereby increasing traffic congestion. As this directly affects driving safety, a new mobility operation system should be developed, and road signs should be constructedto provide guidance. Such a system should first be piloted in Seoul. Third, the variable of real-time guide information based on situation (1.335, 1.350) was significant in Gyeonggi Province, which is part of the SCA, and metropolitan cities. This variable is relevant in situations involving temporary conglomerations (e.g., local events, large-scale conventions, etc.), which are typically arranged in the aforementioned regions. In this context, relevant technologies capable of displaying variable information should be researched and implemented primarily at sites of large-scale events, e.g., convention centers and large parks. Fourth, intensive road sign improvement should be undertaken in the provinces (other cities and counties). In these regions, the average age (0.384) and experience (0.469) of the drivers are relatively high, and the influence of road signs on the driving safety of this demography was observed to be relatively low. Thus, campaigns to raise awareness about driving and promote road sign usage among elderly and experienced drivers are critical. Additionally, nighttime recognition satisfaction (0.823) of road signs was observed to be inversely related to driving safety in provincial areas, indicating the need to improve road illumination in these regions (by installing illuminated signs, improving street lights and guide lights, etc.). Moreover, the influence of route accuracy (1.747) was high in the provinces. Therefore, in these areas, route accuracy should be continuously improved by checking for errors in the guide information of existing signs and ensuring the continuity of guide names.
## 7 Conclusions and Outlook
Similar technologies are applied for the correction of the scan-line corrector (SLC) failure in Landsat 7 ETM \(+\) data (e.g., [50]). Another possible path of image reconstruction could be the estimation of missing backscatter intensity values in SAR images caused by geometric distortions (_i.e._, Layover, Foreshortening and Radar shadow) based on additional optical images.
# Compact MIMO System Performances in Metallic Enclosures
## 2 Measurement Setup
The baseband QPSK receiver was composed of different stages for BER measurements and information recovery. The baseband receiver stages were AGC, coarse-frequency compensation stage, fine-frequency compensation stage, timing recovery, frame synchronization, phase ambiguity resolution, and data recovery stage. We measured the EVM after the fine frequency stage where the received signal compensated for the negative or positive frequency offsets. The BER measurement is a critical key performance indicator (KPI) for a complete end-to-end system performance. The QPSK baseband transmitter and receiver Simulink models were configured for EVM and BER conditions in a MATLAB script. The Tx gain of the USRP changed after the current iterations of the BER and EVM measurements  
Figure 1: Overall USRP-based 2 \(\times\) 2 near-field MIMO measurement setup for BER and EVM measurements. were finished. The BER and EVM measurements were stored in a database for further offline processing and statistical analysis.
## 2 Materials and Experimental Preparation
### Asphalt Mixture for Base Sample  
A kind of styrene-butadiene-styrene block copolymer (SBS) modified asphalt binder with 8% high viscosity modified additive by the weight of asphalt was used in this paper. The main technical indexes were tested according to the test methods in the Chinese Standard Test Methods of Bitumen and Bitumen Mixtures for Highway Engineering (JTG E20-2011) [24]. Its basic properties met the Chinese Specifications for Construction of Highway Asphalt Pavements (JTG F40-2004) [25], as shown in Table 6. The aggregate used was taken from a basalt quarry (Qiangyuan Basalt Development Co., Ltd., Zhuzhou, China). It was completely crushed to give larger particles with an angular shape and rough surface texture. The test methods were based on the Chinese Test Methods of Aggregate for Highway Engineering (JTG E42-2005) [26]. Its basic properties met the Chinese Specifications for Construction of Highway Asphalt Pavements (JTG F40-2004) [24], which are listed in Table 7. In order to make nano-TiO\({}_{2}\) play a better photocatalytic role, open-graded friction course (OGFC) gradation was selected owing to its large air voids and deep structural texture. The design gradation of the aggregate met the requirements of OGFC-10 according to the Chinese Technical Specifications for Construction of Highway Asphalt Pavements (JTG F40-2004) [25], as shown in Figure 1. The designed asphalt aggregate ratio was 4.5%. The results of OGFC design are shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Test Results** & **Requirements [25]** & **Methods [24]** \\ \hline
25 \({}^{\circ}\)C Penetration (0.1 mm) & 42.6 & \(\geq\)40 & T0604 \\ Penetration index (PI) & 1.21 & \(\geq\)0 & T0604 \\ Softening point (\({}^{\circ}\)C) & 91.0 & \(\geq\)80 & T0606 \\
15 \({}^{\circ}\)C Ductility (cm) & 77.3 & \(\geq\)50 & T0605 \\ Toughness (N\(\cdot\)m) & 38 & \(\geq\)20 & T0624 \\
60 \({}^{\circ}\)C Viscosity (Pa-s) & 73,550 & \(\geq\)20,000 & T0625 \\ Flash point (\({}^{\circ}\)C) & 275.0 & \(\geq\)260 & T0611 \\
25 \({}^{\circ}\)C Elastic recovery (\%) & 94.3 & \(\geq\)75 & T0662 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The main technical indexes of high-viscosity modified asphalt.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Test Results** & **Requirements [25]** & **Methods [24]** \\ \hline
25 \({}^{\circ}\)C Penetration (0.1 mm) & 72 & \(\approx\)60–80 & T0604 \\ Penetration index (PI) & 0.11 & \(-\)1.5 to +1.0 & T0604 \\ Softening point (\({}^{\circ}\)C) & 48 & \(\geq\)46 & T0606 \\
15 \({}^{\circ}\)C Ductility (cm) & \(>\)100 & \(>\)100 & T0605 \\
60 \({}^{\circ}\)C Viscosity (Pa-s) & 213 & \(>\)180 & T0625 \\ Flash point (\({}^{\circ}\)C) & 288 & \(\geq\)260 & T0611 \\ Wax content (\%) & 1.2 & \(\leq\)2.2 & T0615 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Basic physical properties of asphalt.