esa-sceva/satcom-chunk-collection huggingface dataset:
# Megalos: A Scalable Architecture for the Virtualization of Large Network Scenarios +
## 2 Related Work and State of the Art
### Orchestrators for Docker  
Container engines are designed to be deployed on a single host. However, modern applications are often composed by hundred of containers that should be replicated and distributed, to satisfy some requirements like scalability and availability. To achieve these goals, container orchestrators have been developed. Briefly, their task is to distribute containers among different nodes of a cluster, following a scheduling algorithm. Also, they ensure that every user-requested container is always running, rescheduling them in case of failures.  
There are currently two main open-source container orchestrators for Docker. Docker Swarm [6] is the Docker built-in container orchestrator. It is an overlay tool that allows to coordinate a multitude of hosts running the Docker engine. Kubernetes [7] is the state-of-the-art container orchestrator, widely adopted in production environments. Being initially developed by Google, it is able to manage distributed clusters composed of thousand of nodes (up to 5k). It supports several container engines, including Docker.  
Since Kubernetes is the de-facto standard among the container orchestrators, we rely on Kubernetes for our architecture.
## 3 Datasets
### Trimble R8 GNSS Survey Data  
A Trimble R8 GNSS receiver [45] was used in the validation tests in this paper. The R8 is a survey grade GNSS rover and is capable of calculating position based on signals from both the GPS and GLONASS constellations. Receivers that can record signals from multiple constellations increase the number of satellites available for a surveyor at any time of day. The receiver was positioned at multiple locations around Maynooth University South Campus for the accuracy tests. A bi-pod was used to minimise any receiver motion during the validation measurements. The user interface on the data-logger displays the number of satellites visible to the receiver at the current time and this was used to validate the prediction results.
## 3 Results and Discussion
### Antioxidant Activity  
The antioxidant activity of _flaxseed_ extract and biosynthesized AgNPs was evaluated using the DPPH method and ascorbic acid was used as a standard reference. The effect of various green synthesized AgNPs concentrations on the DPPH free radical inhibitory is shown in Table 1 and Figure 6. Our results indicate that both _flaxseed_ extract and manufactured AgNPs are effective scavengers of free radicals. However, AgNPs outperformed _flaxseed_ extract in terms of DPPH scavenging efficacy. The AgNPs and extract of _flaxseed_ both increased their DPPH activity dose-dependently. Green-produced AgNPs exhibited scavenging activity ranging from 17% to 97% at doses of 10-100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, with an average IC\({}_{50}\) value of 44.56 \(\pm\) 0.02 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. The antioxidant activity was significantly less than that of conventional ascorbic acid. Our findings are consistent with those of [PERSON], et al., who investigated the antioxidant activity of AgNPs using _Alpinia katsunadai_ seed extract [47].  
There is a difference in the antioxidant potential of _flaxseed_ extract and AgNPs, as demonstrated in Table 1 and Figure 6, and both samples directly reacted and inhibited a variety of DPPH free radicals, and the scavenging activity raised as the concentration of the tested sample increased (46.38 \(\pm\) 2.16%, 53.8 \(\pm\) 2.22%, and 98.12 \(\pm\) 1.31% at 20, 50, and 100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) with flaxseed extract and (28.09 \(\pm\) 0.95%, 62.81 \(\pm\) 2.02%, and 97.4 \(\pm\) 0.92% at 20, 50, and 100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) with AgNPs. Furthermore, AgNPs had the best antioxidant activity; whose IC\({}_{50}\) was 44.56 \(\pm\) 0.02 \(\mathrm{\SIUnitSymbolMicro g}\)/mL compared to 82 \(\pm\) 0.079 \(\mathrm{\SIUnitSymbolMicro g}\)/mL for _flaxseed_ extract.  
Concerning antioxidant capacity, the biosynthesized AgNPs made from flaxseed extract exceeded the extract, and this difference may be explained by the chemical makeup of the various samples used in the experiment [48]. Previous investigations have shown that flaxseed extract has substantial antioxidant activity [49]. The presence of a complex of diverse antioxidant metabolites in plant cells that prevent oxidation and damage to biological components, such as flavonoid and phenolic compounds, may be related to the antioxidant capacity of AgNPs. These compounds formed a coat over the silver nanoparticle and gave it a spherical morphology with a larger surface area after the physicochemical interaction of Ag ions with the functional groups of the plant extract [50].  
\begin{table}
\begin{tabular}{c c c c} \hline  & _Flaxseed_ Extract & AgNPs & **Asorbic Acid** \\ \hline IC\({}_{50}\) (\(\mathrm{\SIUnitSymbolMicro g}\)/mL) & 82 \(\pm\) 0.079 & 44.56 \(\pm\) 0.02 & 26.6 \(\pm\) 0.06 \\ \hline \end{tabular}
\end{table}
Table 1: IC\({}_{50}\) values of _flaxseed_ extract, AgNPs, and ascorbic acid.  
Figure 5: SEM images of green synthesized AgNPs 0.2% flaxseed extract as illustrated in (**A**) low and (**B**) high SEM magnification SEM images, respectively.
## 2 Artificial Neural Networks Topology
### Training Algorithm  
Many training methods have been developed for neural networks [43; 44]; these include the Gradient Descent training approach, the Resilient Back-propagation training approach, the Levenberg-Marquardt (LM) approach, and the Gauss-Newton training approach. The LM training approach has been selected for training both of the developed topologies (see Figures 3 and 4). The LM training method is less sensitive to local converges, and therefore, it provides a better learning training approach. Moreover, it provides a balanced tradeoff between the stability and training speed [43].  
The LM training approach was developed to estimate the derivative of the second-order without the particular Hessian matrix computation requirement. Instead, it only requires the Jacobian matrix, also the gradient vector.  
Equation (5) is used to calculate the loss function gradient vector when the performance function resembles the mean squared error (MSE). Using the Jacobian matrix we can estimate the Hessian matrix as shown in Equation (6) [43]:  
\[\
abla f=J^{T}e \tag{5}\]  
\[H\approx J^{T}J+\mu\,I \tag{6}\]  
In Equations (5) and (6), \(e\) denotes the error vector, \(I\) represents the identity matrix, the combination coefficient \(\mu\) is always greater than zero, and the Jacobian matrix \(J\) can be defined as in Equation (7):  
\[J_{i,j}=\frac{\partial e_{i}}{\partial w_{j}},\quad\textit{where }j=1,\dots,m\,and\,i=1,\dots,n \tag{7}\]  
Equations (5) and (6) can be combined to form the update rule \(\Delta w\) shown in Equation (8) [43]:  
\[\Delta w=\left(JJ^{T}+\mu\,I\right)^{-1}J^{T}e \tag{8}\]  
The state diagram for the training algorithm is shown in Figure 5. The training process begins by calculating the loss (MSE), the Hessian approximation, and the gradient. The training objective is to minimize the loss (MSE) as much as possible. This is done by tuning the parameter (\(\mu\)) of the learning rate as follows:  
* \(\mu\) is multiplied by 10 when the current epoch MSE exceeds the previous value.  
Figure 4: The developed NARX network topology.  
\(\bullet\)\(\mu\) is multiplied by 0.1 when the MSE is equal or less to the previous one.
# A Modified Compact Flexible Vivaldi Antenna Array Design for Microwave Breast Cancer Detection
## 2 Materials and Methods
### Parametric Study of Modified Antenna
#### 2.3.6 Changing Parasitic Element Gap, g  
The gap between the parasitic element and the radiator affects the center frequency location and reflection coefficient values. As shown in Figure 14, g = 0.2 mm is the optimum value for this antenna.
## 2 Related Work
### Motivation  
Data scarcity presents a significant practical challenge in predicting building energy consumption primarily due to the complexities and costs associated with acquiring high-quality labeled datasets. Factors, such as limited sensor deployments [10], privacy concerns [11], and the labor-intensive nature of manual data annotation [10; 12], contribute to this scarcity. This limitation hinders the development of robust predictive models, as insufficient data can lead to overfitting and reduced generalizability across diverse building types and operational conditions. To address this limitation, various recent SSL methods have been extensively explored, in which machine learning, deep learning, or the cluster assumption is employed as the base model to predict output. Such models use statistical learning algorithm or neural network architecture to discover hidden action patterns from training data for prediction. These models do not contain knowledge of the relevant domain, nor do they deal with uncertainties associated with input data. However, domain knowledge is particularly critical for building energy consumption prediction due to the complex interplay of various factors influencing energy use [42]. These factors include building design, occupancy patterns, HVAC systems, and external environmental conditions. Such factors cannot be fully captured from historical energy consumption data or building images [37]. Incorporating domain expertise enables transparent modeling by capturing the underlying physical and operational characteristics of buildings [42]. Some of the SSL methods, studied in the literature, are restricted to image or numerical data classification only rather than numerical data regression. Many of the studied SSL methods do not consider diversity and density while generating unlabeled data in the feature space. In pseudo-labeling based semi-supervised approach, no strong augmentation is performed to reduce noise of the pseudo-label already assigned to the numerical unlabeled data. Moreover, none of the SSL methods we have studied in the literature provide explanation in support of their predictive output.  
To address these shortcomings, this research sheds light on a semi-supervised explainable BRBES framework to predict the energy consumption of buildings. This framework deals with energy consumption domain knowledge and data uncertainties with the BRBES, generates equally distributed unlabeled energy data through weak and strong augmentation, assigns pseudo-labels to the unlabeled data with self-training model, applies strong augmentation to reduce noise of the pseudo-label assigned to the weakly augmented unlabeled energy data, performs regression by predicting a continuous numerical value of the energy consumption in kWh, and explains the predictive output in nontechnical human language through a user interface.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 3 Intersection-Based Link-Adaptive Beaconless Forwarding for City Scenarios
### Routing Protocol Design
#### 3.2.5 Loop Protection  
In urban environment, especially in the presence of streets with loop formation (circular roads), there is chance of having multiple relay nodes in winner states. The proposed ILBFC uses hop-count information to minimize this issue. A relay node in winner state upon reception of a duplicate packet which it is still processing, checks the hop-count information in the packet header before discarding it. If the hop-count of the packet (\(H_{new}\)) is much greater than the one (\(H\)) it is still processing (i.e., \(H_{new}\geq H+\eta\)), it implies that there is another relay node that has already forwarded the very same packet. Here, \(\eta\) is a constant greater than zero. Hence, the current relay node leaves its \"winner\" state and discards the packet. Apart from this, ILBFC sets a maximum hop limit (\(H_{max}\)) that a data packet can traverse and after which it is discarded by the node.  
Figure 2 illustrates the ILBFC architecture and some of its important steps which are as follows: (i) when the source node has a data packet to transmit, ILBFC adds relevant information such as node's location and mobility information, sequence number, etc., to the header of data packet before broadcasting the packet; (ii) upon reception of the data packet, ILBFC determines the suitability of the relay node by checking the above mentioned conditions in order to schedule the packet for further forwarding; (iii) after waiting timer expiration or during default forwarding in case the relay node is in \"winner\" state, it updates the header of data packet adding relevant information and then broadcasts the data packet; (iv) upon data packet reception at the destination node, an ACK packet is transmitted to enable the last hop relay node to cancel its timer.
# A New Mobility Model for Multi-UAVs Reconnaissance Based on Partitioned Zone
## 4 Performance Evaluation
### Coverage Comparison  
Figure 7 shows the experimental results simulated for 7200 s and 10,800 s, which are the times when the proposed model and the Random Waypoint reached the steady states, respectively. The proposed model divides the area into 15 \(\times\) 15 zones, which are appropriate size of the proposed model. We explain this in Section 4.3.  
As shown in Figure 7a, the proposed model provides better results than the other models in terms of the coverage rate. The initial coverage rate is similar, but the times to reach 80% and 90% rate are different, as shown in Table 5. The Random Waypoint model could not satisfy more than 90%, even though simulation time is 7200 s. The 90% reaching time of the Pheromone Repel model is 4193 s while the proposed model takes 3173 s. That is, the proposed model is about 1021 s faster than the Pheromone Repel model for covering 90%, which means 24% performance improvement. Similarly, the 80%-coverage reaching time of the proposed model is reduced by 41.7% and 17.9% compared to the Random Waypoint and the Pheromone Repel models, respectively.  
In Figure 7b, the Random Waypoint model attains 90% coverage rate at 6472 s, which takes more than two times as long as the proposed model. We can see that the Random Waypoint model is not only slow to reconnoiter the new unit region but also scanned the same area repeatedly. The 90%-coverage reaching time of the Pheromone Repel model takes 4208 s and the proposed model takes 3122 s, which means the proposed model has a performance improvement of about 25% compared to the Pheromone Repel model. The main reason of this performance improvement is because the proposed model moves between border zones trying to pass intermediate zones with lower visited numbers.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Specifications** \\ \hline UAVs & 10 ea \\ Simulation area size & 30,000 \(\times\) 30,000 m \\ Simulation time & 7200, 10,800 s \\ zones & \(15\times 15\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.
# Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks
###### Abstract  
With the rapid growth of Internet-of-things (IoT) devices, IoT communication has become an increasingly crucial part of 5G wireless communication systems. The large-scale IoT devices access results in system overload and low utilization of energy efficiency under the existing network framework. In this paper, the cluster head uses the LTE-M protocol, and the intra-cluster uses the low-power wide-area network (LPWAN) self-networking protocol in the wireless sensor network. By a detailed analysis of the messages exchanged between the device and the base station, we describe the causes of overload and the steps of data aggregate combined with the physical channel. Then, we explore the cluster head quantity and the optimal scale in the intra-cluster under the traditional K-mean algorithm. When K is 30 under specific resources, the simulation results show that the system's access success probability and resource utilization are optimal. Also, we propose a distributed dynamic cluster-head selection and clustering scheme based on an improved K-means algorithm. Simulation results show that the proposed scheme can reach 88.07% on the access success probability. The throughput and resource utilization are 3.5 times high than that of the optimal K-means.  
5G; large-scale IoT access; clustering; resource utilization +
Footnote †: journal: Applied Sciences  
_Article_  
Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks
# A Multi-Branch DQN-Based Transponder Resource Allocation Approach for Satellite Communications
## 3 Proposed Method  
This section describes our proposed Multi-Branch DQN (See Figure 3 for an overview). We first define and formulate the resource allocation problem for satellite communications based on our model. Next, we discuss how to model the main elements of reinforcement learning defined in MBDQN. Afterward, the network structure of MBDQN is delineated in detail. Table 1 summarizes the notations used in this paper.  
Figure 3: Overview of the proposed Multi-Branch DQN-based resource allocation method.
## 2 Related Work
### Sensors and Ranging Technology
#### 2.5.5 GNSS/RTK  
Satellite constellations that offer positioning, navigation and timing (PNT) services worldwide are collectively referred to as the Global Navigation Satellite System (GNSS).  
The research conducted by [PERSON] et al. [65] examines several recent developments in modern GNSSs and their potential impact on autonomous driving architectures.  
Also, in [66], [PERSON] et al. explore the fusion of GNSS-RTK/INS/LiDAR through a loosely connected Kalman Filter within the realm of self-driving car applications. More specifically, the research centers around assessing possible LiDAR enhancements by contrasting the speed measurements acquired from the GNSS/INS fusion solution with the LiDAR data.  
RTK stands for Real-Time Kinematic. Real-Time Kinematic positioning offers precise navigation for autonomous vehicles at an affordable cost [67].  
An article by [PERSON] et al. in [68] introduces a \"GPS for a self-driving electric vehicle based on a Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS), and an incremental-encoder odometry system\". Both components are combined into a single system using an Extended Kalman Filter (EKF), achieving accuracy down to the centimetre level. Another research [69], conducted by [PERSON], [PERSON] et al. (2018), included an experimental and comparative analysis to evaluate the performance of the RTK-GPS in situations with flat and unobstructed terrain, uneven and unobstructed terrain, and uneven and obstructed terrain, respectively.  
Figure 6 shows Trimble GNSS/RTK devices. They offer a range of RTK GNSS (Global Navigation Satellite System) receivers, antennas and applications. Trimble's RTK solutions are known for their high accuracy, reliability and robustness.  
Figure 5: (a) IMU—MEMSense; (b) IMU—LORD MicroStrain sensing system.
# A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction
## 4 Results and Discussion
### Prototype Application  
In order to illustrate and visualize the potential of our model, a prototype application for taxi supply demand has been developed in the city of Wuhan. The objective of the application is to integrate some user's spatio-temporal data to provide the application with waiting time predictions for some given user and taxi locations. The software architecture diagram is shown in Figure 12 below.  
Overall, one might note as far as the neural network is appropriately trained, computational times are relatively efficient as demonstrated by the application experiments developed so far. As shown in Figure 13, passengers who take a taxi in front of the university at noon are expected to wait 4.5 min before the first empty taxi can pass, with an accuracy rate of 87.1%; passengers who take a taxi in front of the community at midnight are expected Wait 5.1 min for the first empty taxi to pass, with an accuracy rate of 83.7%; passengers who take a taxi before the small at 3 pm are expected to wait 3.9 min for the first empty taxi to pass, with an accuracy rate of 91.3%. Based on the waiting time prediction and sorting time, the prototype application further suggests the ride point with the highest success rate.  
Figure 12: Software architecture diagram.
# A Comprehensive Worst Case Bounds Analysis of IEEE 802.15.7
## 5 Numerical Analysis
### Scalability Analysis
In this case, we analyze a single packet of data transmitted over the superframe; in such cases, there can be a wastage of bandwidth. For a superframe order of 10 the beacon interval would be 4300 optical clocks, when we employ CAP reduction and join an adjacent superframe, the entire available beacon interval would increase to 6451 optical clocks. In such a case, the nodes have to wait a much larger interval of time to get transmitted. We showcase this technique over six superframe orders, we refrain from showing the higher order superframes as the delay increases very steeply. From Figure 13, it is clearly visible that for the lower superframe orders, the difference in delay against CAP reduction is quite small as the size of the superframe in itself is smaller. When the value of SO increases, the size of the superframe increases along with the total available bandwidth. In cases like the one mentioned before, where only one transmission is taken into consideration, the variance in delay becomes quite significant over the value of the superframe order due to wastage of bandwidth. To avoid such adversities there is need for efficient switching techniques that would switch off and on the CAP reduction to facilitate the network with the required bandwidth. Figure 12: Contention Access Period (CAP) reduction architecture. (**a**) without CAP reduction, (**b**) with CAP reduction
# Learning Spatial-Temporal Background-Aware Based Tracking
## 3 Spatial-Temporal Regularization Background-Aware Correlation Filter
### The Objective Function of the Proposed Model  
Inspired by the above-mentioned analysis, we propose a spatial-temporal regularization background-aware discriminative correlation filter, which is achieved by introducing a spatial-temporal regularization module [24] based on the background-aware filter framework. As shown in Figure 1, the spatial and temporal relation is effectively established between historical frames, and thus gains robustness to appearance changes. Our main objective equation can be expressed as follows:  
\[E(f)=\frac{1}{2}\bigg{\|}y-\sum_{d=1}^{D}Bx_{d}*f_{d}\bigg{\|}_{2}^{2}+\frac{ \alpha_{1}}{2}\|f-f_{d-1}\|_{2}^{2}+\frac{\alpha_{2}}{2}\|f\|_{2}^{2} \tag{2}\]  
where,\(f_{\text{d}-1}\) represents a filter that is learned in a channel in a filter, such as a \(D-1\) channel. \(\alpha_{1}\) and \(\alpha_{2}\) represent regularization parameters. \(\|f-f_{d-1}\|_{2}^{2}\) is the introduced spatial-temporal regularization term, make the filter establish a spatial-temporal relationship between the current and historical frame.