esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Multifunctional Antenna Designs for Internet of Things  
[PERSON]  
Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece; [EMAIL_ADDRESS] (D.G.A.); [EMAIL_ADDRESS] (T.M.E.); [EMAIL_ADDRESS] (T.N.F.K.); [EMAIL_ADDRESS] (M.T.C.)  
2021  
[PERSON]  
Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece; [EMAIL_ADDRESS] (D.G.A.); [EMAIL_ADDRESS] (T.M.E.); [EMAIL_ADDRESS] (T.N.F.K.); [EMAIL_ADDRESS] (M.T.C.)  
2021  
The Internet of Things (IoT) envisions the interconnection of all electronic devices, ushering in a new technological era. IoT and 5G technology are linked, complementing each other in a manner that significantly enhances their impact. As sensors become increasingly embedded in our daily lives, they transform everyday objects into \"smart\" devices. This synergy between IoT sensor networks and 5G creates a dynamic ecosystem where the infrastructure provided by 5G's high-speed, low-latency communication enables IoT devices to function more efficiently and effectively, paving the way for innovative applications and services that enhance our awareness and interactions with the world. Moreover, application-oriented and multifunctional antennas need to be developed to meet these high demands. In this review, a comprehensive analysis of IoT antennas is conducted based on their application characteristics. It is important to note that, to the best of our knowledge, this is the first time that this categorization has been performed in the literature. Indeed, comparing IoT antennas across different applications without considering their specific operational contexts is not practical. This review focuses on four primary operational fields: smart homes, smart cities, and biomedical and implantable devices.  
Internet of Things; multifunctional antennas; 5G systems 2021
# Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture
## 4 Experiments and Results
### Performance Assessment Metrics  
We assess the performance of the enhanced network model in the behavior recognition task, where action detection accuracy serves as the paramount evaluation metric. This experiment specifically emphasizes the evaluation of the enhanced network model in this regard. Detection accuracy is typically appraised using Average Precision (AP) and mean Average Precision (mAP). These metrics offer a holistic evaluation of the model's performance across diverse categories, furnishing crucial insights into the model's proficiency in  
Figure 14: Learning rate change curve.  
recognizing behaviors. mAP (Mean Average Precision) is the mean accuracy calculated as the average of accuracies across all action categories. AP (Average Precision) is computed as the average accuracy for each individual category. The expressions are as follows:  
\[mAP=\frac{AP}{num_{classes}} \tag{25}\]  
\[AP=\int_{0}^{1}\frac{TP^{2}}{(TP+FP)(TP+FN)}dR \tag{26}\]  
In these formulas _TP_ (True Positives) represents the number of instances correctly identified as positive, _FP_ (False Positives) is the count of negative instances wrongly identified as positive, _FN_ (False Negatives) denotes the number of negative instances incorrectly identified as negative, and \(num_{classes}\) represents the total number of behavior categories being assessed.  
The Intersection over Union (_IoU_) is a frequently employed evaluation metric in target detection. It measures the overlap between the predicted frame and the actual labeled frame; specifically, the ratio of their intersection to their union. The expression is as follows:  
\[IoU=\frac{A\cap B}{A\cup B} \tag{27}\]  
\(A\) represents the predicted frame, and \(B\) represents the actual frame. In practical applications, a threshold value is typically defined for _IoU_. When _IoU_ exceeds this threshold, the detection is considered successful. In the context of behavioral recognition, we introduce the concept of _IoU_, which refers to the threshold for the overlapping area between the model's predicted bounding boxes and the actual bounding boxes. In the subsequent experiments, we compare AP and mAP when _IoU_ is set to 0.5.
## 3 Results and Discussion
### Detection of Water Toxicity
#### 3.2.2 Zn as Water Toxicity
Figure 6: (**a**) The correlation function as a function of time for Paramecia under different Zn concentrations shown, (**b**) the correlation time as a function of Zn concentration (mg/L) for Paramecia. *, ** indicated the statistical significance of data \(p<0.05\) and \(p<0.01\). N = 9. Figure 7: (**a**) The cross-correlation function for Euglena under different Zn concentrations, (**b**) the cross-correlation time as a function of Zn concentration (mg/L) for Euglena. *, ** indicated the statistical significance of data \(p<0.05\) and \(p<0.01\). N = 9. However, like Paramecia, Euglena did not show a sudden decrease in correlation time, suggesting the absence of Zn's micronutrient behavior for green microalgae Euglena. However, Euglena exhibits such tolerance behavior up to 2\(\times\) PL for Zn exposure.
## 4 Related Works and Contemporary VSN Projects  
There have been various research works addressing sensor network virtualization but to the best of our knowledge there are no survey papers in the literature. In the past the research community mostly paid attention to different aspects of sensor networks, such as architecture, routing, energy efficiency, security, reliable transmission and data aggregation [1, 2], but recently a good number of related research articles have been published in the field of virtualization of sensor networks [8, 9, 16, 19, 20, 28, 29, 30]. Among the related researches most of them have two approaches. A few researchers have focused on gateway based VSN concepts. In VIP Bridge-based ubiquitous sensor networks [28, 29] the authors have proposed an approach of using bridged to integrate several different sensor networks into one virtual sensor network. Gateway based sensor-grid applications are also discussed in [30, 31, 32]. Other researchers have focused on developing middleware based virtual machines. In [16] the authors proposed a tiny virtual machine for a Sensor Network called Mate. Its code is broken up into small capsules of 24 byte-long instructions allowing complex programs to be under 100 bytes. Mate is implemented on the top of TinyOS [33]. In [27] each application on a sensing device runs inside a sandbox environment where access to hardware resources is only available through the Virtualization Runtime. But in [16] the author proposed virtualization in sensor nodes that have focused on fully virtualizing the host operating system. Instead in [27], author allows the applications to run as native processes in a controlled environment.  
In [25] authors proposed a system called Melete which is based on the Mate virtual machine. The Melete system enables reliable storage and execution of concurrent applications on a single sensor node [25, 34]. Agilla [35] is based on Mate and extends the approach by providing mechanisms for better injection of mobile code into the sensor network to deploy user applications. Impala [36] is a middleware designed for the ZebraNet project [37] and its goal is to enable application modularity, and adaptability to dynamic environments.  
In [21, 38, 39] the authors proposed a simple and robust virtual infrastructure for massively deployed wireless sensor networks that is simple and can be leveraged by a number of different protocols. In [17] the authors discussed dynamic resource discovery and programming of WSNs with logical neighborhoods in details. In [38] the concepts of sensor virtualization for heterogeneous sensor network platforms are proposed. Table 1 summarizes the research directions of sensor network virtualization in different contemporary projects.
## 2 Attenuation Prediction Models
Cloud attenuation from observed visibility values may be predicted by an empirical model estimating the optical extinction coefficient due to scattering, as given in Equation (2).
# Addressing ZSM Security Issues with Blockchain Technology
## 2 Related Work
### The Role of AI/ML in the Implementation of the ZSM Concept  
Artificial intelligence and Machine Learning techniques have been pursued to support the profiling of a service, the forecasting of the quality a service will experience for a given deployment scenario, and the placement of an NFV among others.  
[PERSON] et al. [22], examine the use of Reinforcement Learning (RL) and more specifically, they leverage a Deep Deterministic Policy Gradient (DDPG) RL algorithm to solve the NFV placement problem in a scenario that consists of a Data Center (DC) and multiple Mobile Edge Computing (MECs) infrastructures. The goal is to minimize latency for ultra-Reliable Low Latency Communications (uRLLC). [PERSON] et al. [23], focus on the resource management process in NGNs and the proper network service profiling and placement in order to offer high QoE to the end user. In this work, authors present a framework to address the problem of service profiling and to predict the system's \"critical points\", focusing on complex services running over containers. In [24], the authors focus on the problem of choosing the proper amount of resources to support applications based on VNFs, especially in Mobile Edge Computing (MEC) environments where the computational resources are limited. They argue that AI technology can solve this problem in 5G networks, and they use it to develop a predictive autoscaling mechanism in NFV MANO that could automatically adapt the resources beforehand to the workload used by the application without any human intervention. Authors in this paper leverage Federated Learning (FL) techniques to design deep learning models for predictive Virtual MEC Application Functions (VMAF) autoscaling in a multi-domain setting that can better react to the changing service requirements, optimize the network resource usage, and also comply with data protection policies. Authors in [25] present a framework for Zero Touch Networks that use AI technology and microservices to perform self-orchestration of end-to-end network services. The presented research is part of the European H2020 program calledCHARITY. The goal is to increase the QoE by respecting Key Performance Indicators (KPIs), which are based on NGNs characteristics such as high availability and ultra-low latency. The outcome of this research is an Artificial Intelligence based Resource aware Orchestration (AIRO) framework in Cloud Native Environment that has been tested through simulation. However, the authors do not address the security issues when AI technology is used. This is anticipated to raise new challenges [26] when federated learning approaches enabling different NPs to contribute to the model training will be brought to the scene.
## 4 Results and Discussion
### RCCF Performance Evaluation
#### 4.2.2 Consensus Analysis  
Figure 3 illustrates the relationship between retriever consensus and retrieval accuracy. Our analysis reveals that high-consensus documents (consensus score \(>\) 0.7) achieve significantly higher precision rates (0.89, SciFact) compared to low-consensus documents (0.62), validating the effectiveness of our consensus-based approach.
## 6 Copper Nanoparticles' Toxicity
### Toxicity in Humans  
The four main ways of human exposure to nanoparticles are inhalation, dermal penetration, ocular exposure and ingestion [81]. Depending on particle size, nanoparticles are able to remain in the lung tissue and can cause enhanced oxidative stress and inflammatory responses due to irritation. The authors have determined that copper nanoparticles (23.5 nm) were more toxic than microparticles (17 \(\upmu\)m) because nanoparticles are able to penetrate the body more easily [17].  
Individual copper nanoparticles are able to move between cells or permeate across cellular membranes and eventually enter the blood stream. The circulatory system then plays an important role in spreading the nanoparticles from the initial site of exposure to different parts of the body and eventually accumulating in organs.  
[PERSON] et al. investigated the toxicity levels between CuNPs, CuO NPs and soluble CuCl\({}_{2}\) in leukemic cell line HL60 [82]. They show that CuNPs induced higher toxicity compared to the other two. CuNPs released more ionic copper than CuO NPs and conclude that the higher toxicity is attributed to a combination of both nanoparticles and copper ions. After 2 h of exposure, DNA oxidation, intracellular ROS and mitochondrial damage was observed. Eventually cell death was determined to be through necrosis.  
[PERSON] et al. show that CuO NPs induce cell time and dose dependent autophagy against human breast cancer cells with the formation of auto phagolysosomes [83].
## 3 Numerical Evaluation
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Room size (W \(\times\) L \(\times\) H) & \(5\times 5\times 3\) m\({}^{3}\) \\ \hline Number of transmitter & 1 \\ Location of transmitter & (2.5, 2.5, 3) m \\ \hline LED Lambertian index & 1 \\ LED conversion factor & 0.44 W/A \\ Photodiode responsivity & 0.30 A/W \\ Subcarrier bandwidth & 0.5 MHz \\ Number of subcarriers & 40 \\ \hline Receiver field of view & 90\({}^{\circ}\) \\ \hline Height of receiving plane & 0.85 m \\ \hline Physical area of PD & 1.0 cm\({}^{2}\) \\ \hline Delay \(\tau_{1}\) & 0.33 \(\mu\)s \\ Delay \(\tau_{2}\) & 0.5 \(\mu\)s \\ Delay \(\tau_{3}\) & 0.54 \(\mu\)s \\ Delay \(\tau_{4}\) & 0.58 \(\mu\)s \\ Delay \(\tau_{5}\) & 0.6 \(\mu\)s \\ Delay \(\tau_{6}\) & 1.2 \(\mu\)s \\ Weighting factor \(g_{1}\) & 0.54 \\ Weighting factor \(g_{2}\) & 0.275 \\ Weighting factor \(g_{3}\) & \(-\)0.15 \\ Weighting factor \(g_{4}\) & 0.08 \\ Weighting factor \(g_{5}\) & \(-\)0.03 \\ Weighting factor \(g_{6}\) & \(-\)0.02 \\ Exponent of the attenuation factor k & 1 \\ Attenuation parameter \(\alpha_{0}\) & \(-2.1\times 10^{-3}\) \\ Attenuation parameter \(\alpha_{1}\) & \(8.1\times 10^{-10}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Configuration of main parameters.
# Analyzing Zone-Based Registration Using a Three Zone System: A Semi-Markov Process Approach
## 4 Numerical Results
### Total Cost for Various Probabilities of Moving Back to the Last Zone  
Figure 10 shows the total cost for various probabilities of moving back to the last zone, \(\theta\), when CMR = 1 and \(n=4\). The total cost of 2Z and 3Z systems decrease as \(\theta\) increases but the total cost of the 3Z system decreases more than that of a 2Z system in our environment, as clearly shown in Figure 10.  
Figure 9: Total cost for various CMR.  
From the figure, we can see that, noting that \(\theta\) is generally greater than or equal to 0.25 in a square zone environment, 3 Zs are optimal in most cases.
## 3 Proposed ANFIS Model  
The MATLAB Neural Network Toolbox R2013a (MathWorks, Natick, MA, USA) was used to train and optimize ANNs. ANFIS is an adaptive network which allows the implementation of a neural network topology with fuzzy logic [14; 15]. An ANFIS study compiles these two methods and utilizes the characteristics of both methods. In addition, ANFIS gathers the neural network and fuzzy logic and can address nonlinear and complex problems [16]. ANFIS is a class of adaptive multilayer feed-forward networks, which are functionally equivalent to a fuzzy inference system. The Takagi-Sugeno fuzzy inference system, which contains a five-membership and simple schematic of the proposed ANFIS model, is shown in Figure 3. The proposed model and all tests were implemented using MATLAB. The error function used is a function of the logistic sigmoid and standard total of squared error function.  
The model inputs include the type of material, the width of the crack, and the depth of the crack. The model outputs include the percentage of the signal. The training data were collected experimentally and normalized to obtain the values. The formula used is as follows:  
\[\text{Low}+(\text{High}-\text{Low})\times\text{Actual value}-\text{ Minimum}/\text{Maximum}-\text{Minimum} \tag{1}\]  
Low is the minimum normalized data value, which is equal to 0.1, and minimum is the minimum data value. High is the maximum normalized data value, which is equal to 0.9, and the maximum is the maximum data value [17].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Frequency** & **Gain** & **Probe** & **Persistence** & **Phase** & **Spot X/Y** \\ \hline
100 KHz & 50 dB (approx.) & Bridge & Permanent & Set to 12 o’clock 100\% FSH & Centre of the screen \\ \hline \end{tabular}
\end{table}
Table 1: Weld probe settings.  
Figure 2: Images of commercial ECT system and test specimens.
# A Design Method of Code Correlation Reference Waveform in GNSS Based on Least-Squares Fitting
## 4 Performance Analysis
### Tracking Jitter
#### 4.2.4 BOC(6,1) and BOC(7,1)  
The tracking jitters of the resulting S-curves for the BOC(6,1) signal are shown in Figure 46. It is shown that better results are acquired with a smaller bandwidth, which implies that the amplitude of the reference waveform is generally smaller with a narrower bandwidth. Further, this means that the resulting S-curve for the narrower bandwidth case is more easily realized. The simulation result is shown in Figure 47 for the BOC(7,1) signal.  
Figure 45: The tracking jitters for the S-curves of the BOC(2,1) signal (8.184-MHz bandwidth).  
Figure 46: The tracking jitters for the S-curves of the BOC(6,1) signal.  
In all, the W2 non-coherent discriminator outperforms the simulated LS discriminators in the tracking jitters, except for the case of the BOC(1,1) signal with the 6.138-MHz bandwidth. This is because the complexity of LS waveforms has impacted the tracking precision, which is the same for the HEU S-curve.
# The Synergy of Renewable Energy and Desalination:
## 2 Overview of Desalination Technologies
### Thermal-Based Methods
VC systems can operate effectively with lower energy inputs compared to traditional thermal processes, making them suitable for regions with limited energy resources. Additionally, VC units have a smaller footprint and can be easily integrated into existing facilities. Among the disadvantages is that they may have higher initial capital costs due to the complexity of the equipment involved. Despite these challenges, vapor compression remains a promising technology for providing potable water in areas where conventional desalination methods may not be feasible. Solar stills are simple yet effective devices that harness solar energy to desalinate seawater or brackish water. The basic principle involves placing a shallow basin filled with saline water under a transparent cover. As sunlight heats the water, it evaporates, creating vapor that rises and condenses on the cooler underside of the cover [35; 36]. The condensed droplets then collect and can be harvested as freshwater. This passive method relies solely on solar energy, making it particularly suitable for remote areas with abundant sunlight. The advantages of solar stills include low operational costs and minimal environmental impact. They require no external power source and have low maintenance needs, making them accessible for communities in developing countries facing water scarcity. While solar stills are relatively easy to construct and operate, their production capacity tends to be lower compared to other desalination technologies, limiting their use for largerpopulations or industrial applications. Thanks to advancements in design and materials, they are improving in efficiency and output. Solar stills represent a sustainable solution for addressing water shortages in arid regions. For thermal-based desalination systems to support the evaporation and hydraulic conveyance of several streams, both heat and electrical energy must be used. In comparison to membrane-based techniques, thermal-based technologies offer a number of advantages. The dependability of thermal methods is guaranteed by their widespread recognition and validation. Because of the improved quality of the product, there is less strict observation. A comparison table with some characteristics of various thermal-based desalination technologies is given below in Table 1.
# Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks
## 3 CI-Based Power Allocation Schemes
### GA-Based Power Allocation Scheme  
GA is recognized as a pioneering bio-inspired metaheuristic algorithm from the family of EAs. It follows an adaptive search procedure and is capable of handling multiobjective and multiconstraint numerical optimization problems with a noncomplex formation. GA explores the search space and stores a population of solutions or chromosomes in each generation. The chromosomes compete for resources and the winners produce more offspring, thus propagating their characteristics to the subsequent generations. For this purpose, a fitness value, related to the objective of the optimization problem, is the key criterion for ranking the individuals. A chromosome with a larger fitness value has high probability of resulting in an optimal solution to the problem [37,38].  
After initializing a population of solutions, GA iteratively performs the operations of selection, crossover and mutation to update the population until the stopping criterion is met. The Algorithm 2 summarizes the sequence of steps involved in GA-based optimization.  
```
for \(i=1\):\(MGN\)
1. Tournament selection to choose the fittest individual
2. Crossovers to enrich the population with better chromosomes
3. Mutations to gain diversity and to avoid being trapped in local optima
4. Evaluate fitness function
5. Update local best solution endfor Output: Global best with associated parameters
```  
**Algorithm 2** GA-based Optimization  
Although classic GA is competitive for handling complex and skewed data without prior assumptions, it is not efficient in finding solutions to problems that demand high accuracy [39]. Once GA approximately locates the optimized solution, it moves back and forth continuously to find the optimal solution, which significantly increases execution time and decreases search efficiency. Therefore, in order to increase optimization efficiency, GA has been hybridized with many other algorithms in literature, e.g., dynamic programming, neural networks, etc. [40].
## 4 Model Testing and Results Analysis  
This section presents the used pedestrian and bicycle traffic flow datasets and evaluates the forecasting performance of the proposed method. At first, we verify the one-step forecasting performance of the proposed GAHD-VAE model and compare its improvement with the traditional VAE model. Then, we provide a comparison against the baseline deep learning models, namely LSTM, GRU, BiLSTM, and BiGRU. Furthermore, the impact of using different configurations of the attention model, namely, attention type and activation function at a different level of the proposed architecture, is analyzed. Finally, we evaluate the effectiveness of the considered methods for multi-step forecasting.