esa-sceva/satcom-chunk-collection huggingface dataset:
# MIMO Radar Imaging Method with Non-Orthogonal Waveforms Based on Deep Learning
## 3 Imaging Method of Non-Orthogonal Waveform MIMO Radar Based on Deep Learning
### Network Training Strategy  
Through effective network learning, the imaging network can be driven to automatically learn the mapping from the non-orthogonal waveform echo to the ideal target image from a large number of training samples. In MIMO-LI-Net, the echo transformation module does not contain learnable parameters, so the network only needs to learn the CNN parameters in the feature imaging module. In this paper, the mean square error between the reconstructed target image and the ideal target image is used as the loss function, and the back-propagation algorithm is used to train the network.
## 3 Target Data Processing and Computing
### Computing in the Edge/Fog Layer  
Computing in the edge/fog layer has been introduced to overcome the limitations imposed by centralized cloud computing by performing processing as close to the data source as possible, resulting in a reduction in the round-trip latency when sending data to the cloud and returning the results to IoT devices. It plays an important role in addressing the challenges of real-time and near-real-time data processing and analysis in Internet of Things (IoT) applications. An overview of the layered architecture of edge-fog-cloud computing is shown in Figure 2.  
Edge/fog devices can be considered local or gateway devices that are close to the data source; in some cases, they can be devices at one or two hops along the network to the cloud. Edge devices such as IoT sensors, gateways, and routers execute computations locally, making rapid decisions and providing immediate responses. Applications that need more substantial local processing, data aggregation, and advanced analytics are ideally suited for fog computing. It facilitates the interpretation of data in real time, enabling quicker insights and decisions. While edge computing is best for quick, localized decision making, as it is located closer to the data source, fog computing bridges the gap between the edge and the centralized cloud by enabling more sophisticated processing, aggregation, and analysis. Generally, fog computing has greater processing power and storage capacity than edge computing.  
Initially, fog/edge computing is used as a complement to cloud computing. Small computing tasks and preprocessing tasks that are lightweight are usually executed in this layer before being delegated to the cloud layer. However, the recent increase in the resource capability of low-cost devices offers the potential to use resources as much as possible and provides capabilities for local devices. This can lead to significant achievements in terms of reducing the latency, bandwidth consumption, and the network cost involved in sending large amounts of data to the cloud. As data travel from their point of origin (e.g., sensors) to applications, they pass through many computing devices, each of which is a potential target of computation. By keeping sensitive data localized and lowering the likelihood of data breaches, edge/fog computing improves the security and privacy. In order to ensure that devices can function independently when not connected to the Internet, it also provides offline functionalities.  
Big data platforms play a crucial role in edge and fog computing, offering scalable and efficient solutions to process and analyze vast amounts of data closer to their source. In summary, big data platforms optimized for edge and fog computing environments offer proximity, reduced latency, efficient resource utilization, scalability, flexibility, and support for real-time decision making.
## 7 The Role of Coumarins in the Nutraceutical Quality of Spirit Beverages
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound** & **Pharmacological** & **Research** & **Experimental** & **Doses** & **References** \\ \hline  & & Inhibition of proliferation & \begin{tabular}{c} Human lung \\ cancer cell \\ lines, A427 and \\ Calu-1 \\ \end{tabular} & 1.0 mM & [266] \\ \cline{2-6}  & & Inhibition of proliferation & \begin{tabular}{c} Human lung \\ cancer cell \\ lines, A427 and \\ SK-LU-1 \\ \end{tabular} & 100 mg/mL & [267] \\ \hline Anti-inflammatory & & \begin{tabular}{c} Colonic damage \\ induced by trinitrobenzenesulf \\ fonic acid (TNBS) \\ \end{tabular} & \begin{tabular}{c} Male Wistar \\ rats \\ \end{tabular} & 25 mg/kg & [268] \\ \hline Antibacterial & & \begin{tabular}{c} Minimum \\ inhibitory concentrations \\ (MIC) \\ \end{tabular} & \begin{tabular}{c} _Pseudomonas_ \\ _aeruginosa_ \\ \end{tabular} & 0.66 \(\mu\)g/mL & [269] \\ \hline Antihypertensive & \begin{tabular}{c} Multiple model of hypertension \\ (blood pressure) \\ \end{tabular} & \begin{tabular}{c} Male \\ Wistar-Kyoto \\ rats \\ \end{tabular} & 10 mg/kg & [270] \\ \hline Scopoletin & & \begin{tabular}{c} Streptococcus \\ (STZ)-induced diabetes \\ \end{tabular} & Male ICR mice & 10 mg/kg & [271] \\ Antidiabetic & \begin{tabular}{c} \(\alpha\)-Glucosidase/\(\alpha\)- \\ amylase inhibition assay \\ \end{tabular} & in vitro assay & 100 \(\mu\)M & [271] \\ \cline{2-6}  & & Glucose uptake assay & \begin{tabular}{c} 3T3-L1 \\ preadipocyte cells \\ \end{tabular} & 20 \(\mu\)M & [272] \\ Anti-inflammatory & \begin{tabular}{c} Carrageenannan- \\ induced paw \\ oedema \\ \end{tabular} & \begin{tabular}{c} Female \\ Sprague Dawley rats \\ \end{tabular} & 10 mg/kg & [273] \\ Anti-ageing & \
\begin{tabular}{c} Autophagy assay \\ \end{tabular} &
\begin{tabular}{c} Human lung \\ fibroblast cells \\ (IMR 90) \\ \end{tabular} & 16 \(\mu\)M & [274] \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._In summary, coumarins are one of the most important groups of natural compounds with diverse pharmacological properties, and therefore they have been extensively studied in the field of medicinal chemistry and as therapeutic agents. However, studies involving beverages, and especially spirit beverages, have received less attention.
## 2 Related Research Work
After analyzing the source information in the received probe message, the mobile sink calculates the link connectivity between any two adjacent nodes after determining their neighbor relationships. Thus, an opportunistic connection random graph can be constructed. As SGs require real-time (delay sensitive) and non-real-time (delay tolerant) traffic being routed through the WSN while minimizing the energy consumption and increasing the network lifetime, we need to utilize the WSN routing protocol which can offer energy efficiency and multi-disjoint path routing. Keeping in view these SGs requirements, [PERSON] et al. in [52] discussed the trade-offs between traffic overhead and reliability in multi-path routing for WSN. In [35], the authors discussed the taxonomy of cluster-based routing protocols for WSN, with respect to energy efficient and energy balance routing protocols. [PERSON] et al. discussed the energy efficient and QoS based routing protocol in [53]. [PERSON] et al. in [54] investigated the problem of self-protection in WSNs and devised the Self Protection Learning Automaton (SPLA) algorithm in which sensing graph of the network plays the main role in finding the minimum number of nodes to protect the nodes. The proposed solution takes advantage of irregular cellular learning automaton (ICLA) to properly schedule the sensors into either an active or idle state. In [53], researchers proposed an energy efficient and QoS-based multi-path routing protocol with disjoint paths for real-time and non-real-time traffic management. In [55],[PERSON] et al. presented a novel routing optimization technique based on improvement in Low Energy Adaptive Clustering Hierarchy (LEACH) in WSNs. According to this novel routing optimization technique, energy efficiency per unit node per round can be achieved and network lifetime can be prolonged. In our proposal, we utilized certain ideas from the previous routing protocols in WSN and proposed a detailed solution for optimally tackling the problems like energy consumption and network lifetime enhancement through multi-disjoint path opportunistic node modeling using random graph theory for delay-tolerant and delay-sensitive data in SGs NAN.
## 5 Numerical Simulation Results
### Discussion  
To show the effectiveness of the proposed algorithm in autonomous mobile robot navigation, it was compared with the artificial potential field (APF) method commonly used in a 2D environment, which usually forms a shorter path. As shown in Figure 9a,b,  
Figure 8: The paths of the mobile robot while performing navigation tasks using the trained neural network model in the simulator. (**a**) Case 1; (**b**) Case 2; (**c**) Case 3; (**d**) Case 4.  
the path that is formed by Algorithm 1 proposed in this paper was only slightly longer than the path obtained by the artificial potential field method in the second half without knowing the global environment information, but the generated path was smoother than that of the APF method. Furthermore, the APF method has the probability to fall into the local minimum solution as shown in Figure 9c, while the method based on the neural network is capable of bypassing obstacles of various shapes ahead, as shown in Figure 9d.  
Meanwhile, the dynamic window approach (DWA) is a classic local path planning algorithm. When the robot reaches a certain position, DWA predicts the trajectory of each set of speeds in a limited dynamic window and gives a speed evaluation, and the set of speeds with the highest evaluation is selected as the current speed for execution. Finally, the path planning is transformed into the problem of maximizing the objective function, but real-time computing consumes high computing resources, around 100 times more than the proposed algorithm. Compared to the DWA, the advantage of the method proposed in this paper is that the computation required for neural network training is offloaded to the computer to reduce the requirement on the robotic hardware. When the network is transplanted to the robot, it only needs to perform a few matrix operations to obtain the corresponding action.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL
### Schemes Exploiting Path Diversity
#### 4.2.5 Energy-Aware Load Balancing
Since this work uses the beacon-enabled IEEE 802.15.4 in which beacon frames are sent more frequently than DIO messages, beacon frames are used to inform child nodes of the metrics such as the upstream load estimate and the upstream energy estimate. The work is based on a modified cluster-tree MAC, which was proposed by [PERSON] et al. [32] and permits selecting multiple parents. While constructing a DODAG, each node builds a list of candidate parents by screening out nodes having low RSSI or low upstream energy. If the queue occupancy exceeds a predefined threshold, the probability of parent \(p\) being selected as the preferred parent for the current packet is calculated as follows. \[\Pr(p)=1-\frac{\alpha_{1}\times load(p)}{\sum_{q\in\mathit{Pars}}load(q)}- \frac{\alpha_{2}\times\mathit{dist}(p)}{\sum_{q\in\mathit{Pars}}\mathit{dist} (q)} \tag{21}\]  
where \(\mathit{tPars}\) is the set of this node's parents, \(\mathit{load}(p)\) the upstream load estimate announced by \(p\), and \(\mathit{dist}(p)\) the remaining time to the start of \(p\)'s superframe. Positive coefficients \(\alpha_{1}<\alpha_{2}\) are set so that a higher priority is given to a parent with early superframe start time. If the number of packets in the sender's transmission queue remains below a threshold, \(\alpha_{2}\) is set to 0 so that a parent with a smaller upstream load estimate relays more traffic. Performance was evaluated via WSNet simulation using 144 nodes. At the MAC layer, the beacon-enabled IEEE 802.15.4 was used. The authors compared their scheme to the standard RPL varying the offered load. They evaluated two versions of their work--one using both the upstream load estimate and the superframe distance, the other one using the superframe distance only. In comparison with the standard RPL, both versions achieved a higher PDR and extended the network lifetime for low and moderate traffic load. In terms of delay, the version using only the superframe distance outperformed the standard RPL. The version using both metrics experienced a longer delay than the standard RPL for high traffic load because it selects less congested but longer paths.
# Antimicrobial and Antibiofilm Properties of Bioceramic Materials in Endodontics
## 1 Introduction  
Microorganisms and their by-products are the main etiological factors responsible for pulpal and periapical diseases [1]. Microorganisms found in the root canals are known to form biofilms, which renders them more resistant to antimicrobial agents than bacteria in the planktonic state [2]. The purpose of endodontic treatment is to prevent microbial contamination of the root canal system and to eliminate the microbes from the infected root canal in order to achieve clinical and radiographic success [3]. Although chemo-mechanical preparation significantly reduces microorganisms inside the infected root canal system, proportionally large areas of the main root canal wall remain untouched by the instruments, and it is virtually impossible to completely remove 100% of the microbes by irrigation and other methods [4]. Different methods of irrigant agitation have been introduced, including pipetting, heating the irrigant [5], sonic and ultrasonic activation [6], and multisonic activation [7]. These methods have been effective in promoting the movement of the irrigant to clean areas that are beyond the reach of the instrumentation. Beyond the irrigant agitation, the use of endodontic materials such as sealers, cements, pastes, putty, and obturation materials with antibiofilm activity is considered beneficial in further reducing the residual microorganisms and preventing leakage of a potential reinfection [8].  
Bioceramics are inorganic, non-metallic, biocompatible materials that are used in direct contact with living tissues in the medical and dental fields [9]. Since they are chemically stable, non-corrosive, and interact well with organic tissues, more biocearamic materials have been developed and successfully used in endodontic treatments, including pulp capping, obturation, apical barrier formation, perforation repairs, and root-end filling [10]. Some endodontic biocearamics are powder/liquid systems requiring manual mixing, and some are premixed materials requiring moisture from surrounding tissues to set. The setting process allows the biocearamics to achieve a superior seal with the tooth structure [11].  
The antimicrobial and antib biofilm properties are exerted during the setting process by increasing the pH and ion release from the material [12]. A high-quality seal and antimicrobial properties are both critical to achieving success in endodontic treatment [13]. Antibioticfilm properties may continue to exist in a bioceramic-treated environment via further physico-chemical reactions (e.g., biomineralization effect) with the surrounding dental hard tissues [14]. Meanwhile, challenges remain in balancing the antibiofilm properties with biocompatibility in the development of current bioceramics for endodontics. This review takes a critical look at the antimicrobial and antibiofilm properties of currently available bioceramic materials used in endodontics and looks forward to embracing possible new technologies that may facilitate the elimination of all microorganisms for long-term endodontic success.
# Research on Multi-Objective Evolutionary Algorithms Based on Large-Scale Decision Variable Analysis
## Appendix A Penalty Boundary Intersection (PBI)  
The Penalty Boundary Intersection (PBI) [11] method is one of the commonly used aggregation functions in the MOEA/D algorithm. Equation (A1) shows the calculation method of the PBI algorithm. Here, \(\omega\) represents the decision space, \(x\) is any point in the decision space, \(z^{*}\) is a vector composed of the optimal values of each dimension, known as the ideal point, and \(W\) is the reference vector. The point \"P:F\" (\(x\)) represents the vector of \(x\) in the objective space. \(d_{1}\) is the projection length of the line connecting point \(P\) and the origin on the weight vector \(W\), indicating the proximity of the individual to the Pareto front. \(d_{2}\) is the distance from point \(P\) to the weight vector \(W\), indicating the degree of deviation of the individual in the direction of the weight vector.  
The application of PBI in the proposed DVQA method aims to leverage these metrics to quantitatively analyze the behavior of decision variables, thereby improving the accuracy and efficiency of the optimization process in high-dimensional multi-objective optimization problems.  
This approach intends to provide a structured way to analyze decision variables' contributions to both convergence and diversity, ultimately aiming to enhance the robustness and effectiveness of the multi-objective evolutionary algorithms (MOEAs) in dealing with large-scale optimization problems.  
\[\min g^{\textit{P}bi}(\mathbf{x}|\mathbf{w},\mathbf{z}^{*})=d_{1}+ \theta d_{2}\quad\text{subject to}\quad\mathbf{x}\in\Omega \tag{10}\] \[\text{where}\quad d_{1}=\frac{\|\mathbf{F}(\mathbf{x})\cdot \mathbf{w}\|}{\|\mathbf{w}\|},d_{2} =\left\|\mathbf{F}(\mathbf{x})-d_{1}\frac{\mathbf{w}}{\| \mathbf{w}\|}\right\|\]
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 3 Methods  
The Adaptive KNN-Ensemble Pilot Workload Detection (AKE-PWD) model is proposed. It adopts a strategy of soft voting ensemble learning, with random forest, gradient boosting trees, and an FCN-Transformer model as the base learners. In order to solve the problem of individual differences regarding physiological features, the K-nearest neighbors (KNN) algorithm is introduced in the outer layer of the model.  
During data preprocessing, all features were clustered into four distinct categories using the spectral clustering technique. For the model construction, first, the KNN algorithm identifies the physiological feature cluster to which a sample data belong. After that, the corresponding ensemble model is then selected to perform feature extraction and classification prediction for the identified cluster, thereby increasing the robustness and cross-pilot generalization of the model. The model structure is shown in Figure 1.
###### Abstract
* The enhancement of system interpretability through a multi-stage DL approach, distinguishing it from monolithic DL models. This distinctive feature substantially diminishes the 'black box' nature often associated with DL systems. * The integration of DL techniques to dynamically estimate the energy detection threshold. To the best of our knowledge, although AI-based methods for spectrum sensing exist, the utilization of DL techniques to adaptively determine the threshold in energy detection has not been extensively explored in the literature; thus, it represents a novel contribution. * Our exclusive use of the time domain samples eliminates the need for typically resource-intensive operations and transformations, although it presents additional challenges. Certain transformations excel at extracting valuable features, some of which exhibit remarkable performance even under severe channel impairments. Nonetheless, our system effectively addresses these challenges within the time domain. * The exploration of diverse DL architectures, expanding beyond conventional choices such as CNN and LSTM. The incorporation of autoencoders serves as a bridge toward the wider integration of generative AI for the future in solving spectrum sensing problems. The subsequent sections are organized as follows: Section 2 provides a comprehensive overview of the system model, energy detection, proposed neural network architectures, training and testing datasets, hardware setup, hyperparameter settings, as well as the block bootstrapping method, which is one of the various methods utilized for performance measurement. Section 3 presents the results obtained from the experimental analysis. Finally, Section 4 concludes the paper by discussing the findings in detail and highlighting their implications.
# Algorithms for Delivery of Data by Drones in an Isolated Area Divided into Squares
## 2 Materials and Methods
### Algorithms and Protocols for Delivery of Data Using Drones
For each data file that has to be delivered, a \"json\" file is attached that stores all of the information needed to transfer the file from the source to the destination: delivery type (Dijkstra, Epidemic etc. ); file information (name, size, time-to-leave); route information (node codes: stations and drones); etc. The json files that store message information for the algorithms Epidemic, Spray and Wait, PRoPHET, MaxProp, and MaxDelivery implemented in the ONE environment are similar to those considered in [41]. Each route starts and ends with a station id. Each station id (except for the destination) is followed by a drone id, and each drone id is followed by a station id. When a drone arrives at a station, a transfer is initiated between the drone and the station. The drone transfers to the station all of the files that have the station's id in the attached json files. After the drone transfer to the station is completed, the station transfers to the drone all of the files that have the drone's id in the json file. The range of the Wi-Fi boards was tested. The connections between the boards and file transfer were performed at a distance of up to 85 m with no obstacles in between. In Figure 9, the console output of the Wi-Fi board is presented. The following steps are executed: setup (upload speed is set to 115,200, MAC address is obtained), the connection between two Wi-Fi boards is established, the file transfer is performed, and, finally, the Wi-Fi boards are disconnected. In our model, when transferring files, the drone hovers over the station at a height of 30 m, which is significantly less than the maximum distance obtained in range tests. The transfer speed, including writing on and reading from the SD card, was also tested at a distance of 30 m. An average of 5.81 Mbps was obtained, which means that a file of 10 MB was transferred in 13.77 s.  
Figure 8: Pseudo-code for the construction of the route after the execution of the TD-Drone Dijkstra algorithm.
# Performance Analysis of the Pilot and Data Component of a CSS Signal for LEO-PNT +
## 2 Signal Model  
In this section, we introduce the signal model for the proposed CSS-based PNT signal in this paper. For that, let \(T_{\mathrm{c}}\) be the chirp period or duration and \(B\) be the signal bandwidth. Then, a general expression for a CSS signal is given by the following waveform:  
\[s(t)=e^{\int_{0}^{t}[f_{0}+\mu u]_{B}\mathrm{d}u}, \tag{1}\]  
with \(f_{0}\) as the initial frequency, \(\mu\doteq T_{\mathrm{c}}/B\) as the chirp rate, and \(\left[\cdot\right]_{B}\) as the modulus-B operator. For a PNT signal, we have to consider the presence of data information (e.g., represented by \(f_{0}\)), time delay (\(\tau\)), and frequency Doppler (\(f_{\mathrm{D}}\)). The challenges of a CSS-based PNT signal comes from the traditional de-chirp process used in radar and communications [4]. Traditionally, the frequency of the de-chirped signal has been used to estimate either time delay, frequency Doppler, or data information. In a PNT scenario, all the parameters of interest (i.e., \(\{f_{0},f_{\mathrm{D}},\tau\}\)) are embedded on the de-chirped frequency [4]. This scenario leads us to the transmission of two separate components: the pilot and data component. The pilot component does not carry data information, and it is used to synchronize the signal. Once the signal is synchronized, the data component is used for the reception of the useful information needed for PNT. Second, in the act of synchronizing the signal, we will extract the ranging information (i.e., time delay) needed to localize the user.  
Finally, it is worth noting that the design of a GNSS signal must include a multi-satellite access (MSA) scheme. These concepts are introduced in the following.