esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
## 2 Background and Related Work
### Agent-Based Models  
Agent-based models are computer simulations used to study the interactions between people, things, places, and time [35]. They are often used to understand complex phenomena that emerge from individual behaviors and interactions.  
Agent-based models have been applied to study jamming attacks in various network environments. For example, some researchers have used agent-based models to analyze how jamming attacks affect network traffic [36], how negotiation mechanisms can defend against jamming attacks in smart grid power markets [37], or how machine learning techniques can detect jamming attacks in 5G networks [38].  
Multi-agent systems have also become a prevalent approach in WSN research due to their compatibility with the distributed nature of WSNs. For instance, [PERSON] et al. [39] described a method for designing a multi-agent system for structural health monitoring utilizing a wireless sensor network, along with a specific platform dedicated to the network. [PERSON] et al. [40] proposed a strategy for optimizing WSN performance using multi-agent systems that consider various parameters, including energy, distance, and delay.  
When considering research solely focused on jamming in multi-agent WSNs, only two studies [41; 42] utilizing game theory as an analytical method were identified. [PERSON] et al. [41] propose a novel anti-jamming method based on multi-agent reinforcement learning for wireless networks under jamming attacks. The method uses a partially observable Markov decision process to model the interactions between legitimate nodes and jamming nodes and applies a decentralized Q-learning algorithm to learn the optimal anti-jamming strategy. The paper shows that the proposed method can achieve better performance than existing methods in terms of throughput, convergence speed and robustness. [PERSON] et al. [42] propose a novel game-theoretic framework based on multi-agent deep reinforcement learning for anti-jamming in wireless networks. The paper uses a generative adversarial network to generate realistic jamming signals and a policy gradient algorithm to learn the optimal anti-jamming strategy. The paper shows that the proposed method can achieve better performance than existing methods in terms of throughput and robustness.
## 4 Methodology of APSO-LS
### Rolling Horizon Framework  
The rolling horizon framework divides the mission into \(H\) stages, each corresponding to a specific time interval. At the beginning of each stage \(h\), the system observes the current set of tasks \(\mathcal{D}_{h}\), their workloads \(L_{j}^{h}\), and the positions and energy levels of the UAVs. The APSO-LS algorithm is then employed to solve the optimization problem for stage \(h\), determining the task allocation \(W^{h}\) and routes \(\mathcal{R}^{h}\) that minimize the objective function \(J^{h}\).  
Between stages, new tasks may be added to the system (up to a maximum of \(N_{D_{\max}}\) tasks), or the workloads of existing tasks may increase by a predefined rate \(\delta\). This dynamic update is incorporated into the next stage's optimization, ensuring that the UAVs continuously adapt to the evolving environment.  
The key advantage of the rolling horizon framework is its ability to handle uncertainty and dynamism by replanning at regular intervals, thus avoiding the computational intractability of solving the entire dynamic problem at once.
## 3 Experiment
### Import Machine Learning
#### 3.2.4 KNN  
The K-Nearest Neighbor (KNN) algorithm, a non-parametric approach, forecasts the classification of a given sample by considering the classes of the K nearest neighbors within its vicinity [25]. The crux of employing this model hinges on the selection of an optimal K value, which significantly impacts the prediction accuracy of the model [26]. The range of K values was established from 1 to 29 for experimental purposes. To achieve the most favorable outcome, the test size parameter was adjusted to 0.3. The implementation code is presented in Algorithm 4.  
```
1:data \(\leftarrow\) read csv(\"stopbang\")
2:X \(\leftarrow\) data.drop(['OSA'])
3:y \(\leftarrow\) data['OSA']
4:Xtrain, Xtest, Ytrain, Ytest \(\leftarrow\) train test split(X, Y, test size = 0.3, random state = 42)
5:for k in [1,..., 29]do
6:model \(\leftarrow\) KNeighborsClassifier(n neighbors = k)
7:model.f it(Xtrain, Ytrain)
8:Ypred \(\leftarrow\) model.predict(Xtest)
9:cm \(\leftarrow\) confusion matrix(Ytest, Ypred)
10:TN, FP, FN, TP \(\leftarrow\) cm.ravel()
11:sensitivity \(\leftarrow\) TP/(TP + FN)
12:specificity \(\leftarrow\) TN/(TN + FP)
13:accuracy \(\leftarrow\) accuracy score(Ytest, Ypred)
14:Ypred proba \(\leftarrow\) model.predict proba(Xtest)[:, 1]
15:auc \(\leftarrow\) roc auc score(Ytest, Ypred proba)
16:f1 \(\leftarrow\) f1 score(Ytest, Ypred)
17:Print(\"Accuracy:\", accuracy)
18:endfor
```  
**Algorithm 4** K-Nearest Neighbor for OSA Diagnosis
# How to Isolate Non-Public Networks in B5G: A Review
###### Abstract  
Non-public networks (NPNs) have drawn much attention due to their flexibility and efficiency with B5G. According to the requirements of various application scenarios, NPNs can be tailored to a number of deployments, sharing the B5G system totally or in part. The isolation of NPNs is a critical issue. This paper provides a survey of isolation schemes for B5G NPNs. First, we present an overview of various deployments and the corresponding isolation demands for B5G NPNs. To meet these isolation demands, three kinds of NPN isolation--i.e., spectrum isolation, RAN isolation and CN isolation--are discussed. Then, the corresponding isolation technologies are introduced and analyzed. Finally, open research challenges, such as wireless throughput capacity with spectrum isolation, operation with isolation requirements and data isolation of software-defined CN for B5G NPNs, are discussed.  
NPN; isolation; B5G
# Evaluating Soil Bacteria for the Development of New Biopreparations with Agricultural Applications
## 3 Results
### Biochemical Characterization of Soil Bacteria
#### 3.2.4 Total Sugar Produced by Soil Bacteria in Batch Cultures  
Total reducing sugars (TRS) (Figure 4) were determined in post-culture liquids after 5-day submerged cultivation with maize, winter barley, or trtictale residues.  
The highest amounts of TRS released from the polysaccharide structure of the post-harvest residues after 5 days of incubation were observed for strains WK-9, WK-13, WK-14, WK-15, and WK-20. However, only four strains (WK-9, WK-14, WK-15, and WK-20) increased TRS for all three types of crop residues, indicating synthesis of more efficient or more versatile enzymes or enzyme combinations. Reducing sugars were not detected in any samples inoculated with either WK-7 or WK-8. The absence of TRS in those liquids could be a result of the low enzymatic activity of those isolates, when cultivated in the presented conditions (results in Figure 3). The low enzymatic activity of those bacteria (in comparison to other strains) and therefore low content of TRS in post-culture liquids could be due to the non-optimal conditions for WK-7 and WK-8 cultivation chosen for this study (composition minimal mineral medium, temperature, or time of the incubation) or due to the lack of enzymes able to catalyze degradation of complex substrates, such as maize, trtictale and winter barely residues.  
The five most promising strains for the biodegradation of post-harvest residues were selected for further research: _P. megaterium_ WK-5, _P. amylolytica_ WK-9, _B. velezensis_ WK-14, _B. subtilis_ WK-15, and _B. licheniformis_ WK-20.  
Among them, _B. subtilis_ and _B. licheniformis_ are producers of enzymes with the GRAS (Generally Recognized as Safe) status granted by the FDA (Food and Drug Administration) [(43)]. In addition, the strain belonging to _B. velezensis_ has been assessed by the EFSA (European Food Safety Authority--EFSA) as safe for use as a feed additive for pigs and has been granted the QPS (Qualified Presumption of Safety) status [(44)]. _Paenibacillus amylolyticus_ and _Prestia megaterium_ do not have GRAS or QPS status, but we have found that they do not pose a risk to human health under Directive 2000/54/EC of the European Parliament and of the Council of 18 September 2000 on the protection of workers from risks related to exposure to biological agents at work [(45)]. These strains are not listed in groups 2-4 of bacteria for workers' health hazards and therefore are unlikely to cause adverse health effects on humans under the terms of the Directive.  
Figure 4: Concentrations of total reducing sugars in post-culture filtrates of WK strains cultivated with various crop residues for 5 days.
## 2 GNSS Stations and Data Processing
### Methodology Employed
The data were represented in Python 3 [46] thanks to PyGMT library (Python interface for the Generic Mapping Tools) [47]. The final strain-stress results were obtained with the Optimal Interpolation of Spatially Discretized Geodetic Data algorithm [48] from thepreviously calculated GNSS velocities. The Global Multi-Resolution Topography (GMRT) Synthesis databases were used to represent the topography of the maps [12].
## 3 Methodology  
### Problem Formulation  
The supply chain inventory optimization problem can be formulated as a Markov Decision Process (MDP) to enable the application of reinforcement learning techniques, such as the Soft Actor-Critic algorithm with prioritized experience replay. The goal is to learn adaptive strategies that can effectively handle demand uncertainty and optimize inventory levels across the supply chain network.  
In the considered supply chain network, there is a single factory and multiple warehouses (K in total), as depicted in Figure 1. The demand for each time period is unknown, and the objective is to determine the optimal production quantity at the factory and the distribution quantities to each warehouse to maximize the overall reward. The state space of the MDP is defined as st = [s0, s1, s2, \(\ldots\), sK, dt], where s0 represents the stock level at the factory, s1 to sK represent the stock levels at each warehouse, and dt is the demand history. The action space consists of the total production quantity and the distribution quantities to each warehouse.  
The one-step reward function is designed to capture the costs associated with production, storage, and transportation, as well as the revenue generated from satisfying customer demand. The state transition function models the dynamics of the supply chain network, considering the production, distribution, and demand fulfillment processes.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 1 Introduction
Furthermore, in order to improve network performance in the presence of hidden nodes, one solution is presented, which has been chosen not only according to effectiveness and ease of implementation criteria, but also taking into account its ease of integration in IEEE 802.15.7 standard, since it requires no modifications. Our proposed solution is based on selecting the type of idle patterns to be sent by the network coordinator, depending on whether or not it is receiving transmissions coming from network devices. According to IEEE 802.15.7 standard specification, sending idle patterns is a mandatory requirement for infrastructure nodes while idle or receiving operations, to ensure continuous illumination and avoid flickering. The rest of this paper is organized as follows: Section 2, describes the fundamentals of VLC channel modeling, and the main causes of the hidden node problem are also analyzed. Next, Section 3 provides an overview of the IEEE 802.15.7 standard. In Section 4, we present a description of the simulation model implemented to analyze and evaluate the performance of the CSMA/CA channel access mechanism defined in IEEE 802.15.7. In Section 5, simulation results obtained with and without the presence of hidden nodes are analyzed and compared. In Section 6, a solution to the hidden node problem is proposed, and its effectiveness is analyzed. Finally, Section 7 contains the conclusions of this work.
## 3 Results and Discussion
### Volatile Compounds in Mandarin Juices and Juice Powders  
Volatile compounds play a critical role in fruit flavour and, thus, have been considered to be an important contributor to the sensory quality of fruit products. However, due to thermal treatment, key aroma compounds, such as esters, terpenes, aldehydes, and alcohols, could easily escape from the original fruit juices during spray drying. In this study, a total of 26 aroma compounds were quantified in all of the mandarin samples (Table 4). These compounds were grouped into three categories, namely terpenoids, aldehydes, and alcohols. Terpenoids are important compounds for plants to defend against pathogens, insects, and competitors, as well as to attract seed disseminators and pollinators ([PERSON], 2017). They have also been reported as major volatile compounds in mandarin juice as well as the essential oils that were extracted from mandarin peels ([PERSON], 2017). The current study revealed a similar finding that terpenoids were the most abundant group, comprised of 21 compounds in both the original juices and reconstituted juice samples with the concentrations ranging from 12,355 \(\upmu\)g/L to 864,558 \(\upmu\)g/L. Among the 21 terpenoids that were detected in this study, five of them, namely \(\upalpha\)-cubebe, _cis-\(\beta\)_-elemene, \(\beta\)-famesene, \(\alpha\)-famesene, and nerolidol, belong to sesquiterpenes, while the rest are assigned to monoterpenes.
# An Overview of Intelligent Transportation Systems in Europe
## 5 Key Features of ITSs
### Challenges and Obstacles in the Implementation of ITSs in Europe
#### 5.2.6 Synergies with Other Sectors  
In Europe, there is a continuous effort to promote cooperation between the transport and aerospace domains to improve timing and positioning services needed for ITS applications. Although the European Union Space Programme provides vital space-based information, the full potential of these collaborations has not yet been fully exploited [72]. To maximize the benefits of digitalization in the road sector and to facilitate the faster deployment of ITS services across Europe, it is essential to address issues related to interoperability, cooperation and data sharing [73].
# Effect of Solid Particles on Droplet Size Applying the Time-Shift Method for Spray Investigation
## 1 Introduction  
Although spray processes are often applied in industrial production, the influence of process conditions and liquid properties on atomization is not yet fully understood. In particular, knowledge remains limited on the twin-fluid atomization of suspensions with different viscosities and solid mass fractions, as commonly used in the food industry (e.g., in spray drying), gas cleaning or in entrained flow gasifiers (EFG). In an EFG, oxygen serves as the gasification and atomization agent, leading to Gas-to-Liquid Ratios \(GLR<1\). This limitation necessitates precise understanding of the mechanisms behind the primary breakup of the suspension jet and the resulting droplet size. Investigations of droplet size distributions in suspension sprays have typically used the laser diffraction technique due to the opacity of the emerging droplets [1; 2; 3]. Applying this measuring technique results in an integral droplet diameter that represents the spray quality in the measuring plane. Additionally, suspensions mostly exhibit non-Newtonian behavior, wide particle size distributions and varying solid mass fractions [4; 5]. As a result, a targeted analysis of the influence of solid particles on spray formation that excludes liquid viscosity has not been possible thus far, which this study aims to redress.  
This article aims to make an isolated investigation of solid particles on spray formation independent of fluid viscosity. The work is based on the local measurement of droplet size distributions in opaque suspension sprays. Therefore, Newtonian suspensions with varying particle mass fractions and pure liquids with identical viscosities are used. Spatially resolved droplet size distribution measurements are performed by means of the time-shift technique and compared to state-of-the-art laser diffraction measurements.
## 2 Background
### Hardware-in-the-Loop Simulation  
A HIL simulation is a technology used to develop and evaluate complex real-time systems, particularly in the automotive industry, enabling specialized ECU or ECU networks to be developed and evaluated under realistic conditions. A HIL simulator is a tool that provides the functionality of the rest of the vehicle systems such that a specialized ECU can be tested under realistic conditions. Since a HIL simulator runs in real-time and has variable in-vehicle network interfaces, actual hardware and software ECUs can be closed-loop tested for the entire vehicle systems through interaction with dynamic models implemented in a virtual environment. Furthermore, the hardware and software ECUs can be used for field tests after successful HIL test. A well-developed simulation testbed using an HIL simulator provides a cost-effective and safer environment for developing and evaluating vehicle systems.  
Previous studies have tested ECUs by integrating a HIL simulator with a driving simulator. [PERSON] et al. [24] designed the HEV model and ECUs to evaluate the performance of the HEV system using a driving simulator and a HIL simulator. V2X communication is required in the connected vehicle (CV) system, and the V2X communication device can be used to implement the HIL simulation testbed of the CV system. [PERSON] et el. [25] integrated a HIL simulator and a vehicle dynamics simulator with a V2X communication device to implement a HIL simulation testbed to develop a connected and automated vehicle (CAV) application, and proposed a green-wave algorithm for eco-driving, which improves the fuel efficiency of the CAV by using V2X communication. However, they did not consider the V2X communication channel characteristics, and thus they operated the algorithm by assuming the successful reception of V2X messages. V2X communication channel characteristics such as the communication delay and error rate depending on the driving environment must be considered to accurately evaluate the CV system, which is possible by integrating it with the network simulator. Therefore, in the proposed IDHIL simulator, a V2X communication device combined with a network simulator through a packet emulation function was integrated with the driving and HIL simulator for HIL testing of the V2X communication device in a realistic environment.
# IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing
## 3 The Proposed Method
### Network Design
#### 3.2.1 Multiscale Feature Extraction  
Multiscale feature extraction is very effective in the field of dehazing, while maintaining scale invariance and extracting information [42; 43; 44; 45]. Moreover, parallel convolutions with different filter sizes are used to capture features at different scales. To compensate for the loss during information convolution, we connect network features of different scales to each other before extracting information from the next feature layer. Inspired by feature extraction methods, we used convolutional layers of different sizes to densely extract the features of the input image at different scales. As depicted in Figure 2, we choose to use five convolutional operations in the multiscale feature extraction block of IFE-Net, where the size of any convolution filter is among \(1\times 1\), \(3\times 3\), \(5\times 5\), \(7\times 7\), and \(9\times 9\). \"Conv1\" uses a \(1\times 1\) convolution kernel to extract features, while \"Conv2\" uses a \(3\times 3\) convolution kernel to extract features; then, \"Conv1\" and \"Conv2\" layers are concatenated into the \"concat1\" layer. During the forward propagation process, a \(5\times 5\) sized convolution kernel is used to extract features from the \"concat1\" layer to obtain the \"Conv3\" layer. The \"Conv2\" layer, and the \"Conv3\" layer are concatenated into the \"concat2\" layer, and a \(7\times 7\) sized convolution kernel is used to extract features from the \"concat2\" layer to obtain the \"Conv4\" layer. Then, the \"Conv3\" layer and the \"Conv4\" layer are concatenated into the \"Concat3\" layer, and a \(9\times 9\) convolution kernel is used to extract features from the \"concat3\" layer to obtain the \"Conv5\" layer. Finally, the \"Conv1\" layer, \"Conv2\" layer, \"Conv3\" layer, \"Conv4\" layer and \"Conv5\" layer are concatenated to obtain the output of the multiscale feature feature extraction block. Importantly, the multiscale design of IFE-Net reduces information loss during convolutions and captures features at different scales.
## 7 Conclusions  
Given the great significance of medical image recognition and the particular problem of small medical image datasets, the focus of this work is on how a CNN model (i.e., MobileNetV3) could be implemented with feature selection optimization for small datasets, and the performance was evaluated. Initially, the features of the small medical image datasets were obtained by using a MobileNetV3 model, which is considered the most recent transfer learning model, and the MobileNetV3 method is compared with other high-efficiency methods. The proposed approach was improved in order to create more relevant feature vector representations that are beneficial to the medical field by using medical imaging datasets. Moreover, a unique metaheuristic technique based on selected features combined the Artificial Hummingbird Algorithm (AHA) with Aquila Optimization (AO) to select the relevant features. To check whether our approach worked well, it was tried on the following datasets: the ISIC-2016, PH2, Chest-XRay, and Blood-Cell datasets. The findings revealed that the proposed optimization strategy outperformed existing feature selection techniques that are currently in use. Furthermore, tests with several other cutting-edge medical image classification systems revealed that the proposed approach is a good procedure. Future studies will examine the growing availability of medical data and their use in medical care. The combination of differ ent categorization algorithms is also a fascinating research issue, as it might help practitioners enhance the performance of the present methods.  
Conceptualization, M.A.E., A.D. and A.M.; methodology, M.A.E., A.D. and A.M.; software, M.A.E., A.D. and A.M.; validation, M.A.E., A.D. and S.E.-S.; formal analysis, M.A.E., A.D. and S.E.-S.; investigation, M.A.E., A.D. and S.E.-S.; writing--review and editing, M.A.E., A.D., S.E.-S., A.M. and M.M.G.; visualization, M.A.E., A.D., S.E.-S., A.M. and M.M.G.; supervision, M.A.E. and M.M.M.G. All authors have read and agreed to the published version of the manuscript.  
This work has received financial support from the European Regional Development Fund (tRDF) and the Galician Regional Government, under the agreement for funding the Atlantic Research Center for Information and Communication Technologies (atlanTlic). This work was also supported by the Spanish Government under re-search project \"Enhancing Communication Protocols with Machine Learning while Protecting Sensitive Data (COMPROMISE)\" (PID2020-113795 RB-C33/AEI/10.13039/501100011033).  
The data available upon request.  
The authors declare that there are no conflicts of interest regarding the publication of this paper.