esa-sceva/satcom-chunk-collection huggingface dataset:
# Federated Learning for Secure In-Vehicle Communication
## 2 Backgrounds and Preliminaries
### Attacks Types
#### 2.2.2 Spoofing Attack  
Spoofing or Malfunction attack involves imitating authentic ECUs or users in the IoV network. This allows a malicious actor to inject fake commands, which can lead to congestion of traffic or accidents [21].
## 1 Introduction  
Recently, numerous researches have focused on adsorptive arsenic removal, in which iron oxide was considered to be an appropriate adsorbent due to its high affinity [1; 2; 3; 4; 5; 6; 7]. However, the As(III) adsorption is less effective than the As(V) adsorption by adsorbents in natural water. Arsenite [As(III)] is much more toxic, soluble and mobile than arsenate [As(V)]. Manganese oxides have been extensively investigated as oxidizing agents for arsenite; the reaction is written as follows [8; 9; 10]:  
\[\mathrm{MnO_{2}+H_{3}AsO_{3}+2H^{+}=Mn^{2+}+H_{3}AsO_{4}+H_{2}O}\]  
Accordingly, a novel binary oxide concept in which the Mn-O catalyzed the As(III) pre-oxidation to As(V) and the Fe-O functioned as adsorbent, was therefore proposed [11; 12]. To our knowledge, the Mn-Fe binary oxide materials was easily prepared by the co-precipitation methods, where the Mn reduction by ferrous is dependent on the solution pH [12; 13]:  
\[\mathrm{3 Fe^{2+}+MnO_{4}^{-}+4 OH^{-}+3H_{2}O\to 3 Fe\;(OH)\;_{3}(s)+MnO_{2}(s)+H^{+}}\]  
However, the powder product of micrometers in size was difficult for solid-liquid separation. Based on the perspective of Mn-Fe binary oxide for arsenic removal, this study applied a millimeter scale iron oxide (BT-3) as support and aims at the synthesis of manganese oxides on its surface through the redox of potassium permanganate \({}^{+}\) in a fluidized bed reactor (FBR). The FBR technology could immobilize potassium permanganate onto BT-3, which was a waste iron oxide from real fluidized bed-Fenton (FBR-Fenton) instruments and was mainly composed of poorly crystallized goethite [14].  
Therefore, the employment of BT-3 as the support had the advantages of being cost-efficient and facilitated easier separation and disposal during the synthetic process of binary oxide, and during the application for arsenic removal. Our previous studies have explored the ability of BT waste iron oxide as an excellent adsorbent for \(\mathrm{F^{-}}\), \(\mathrm{PO_{4}^{3-}}\), \(\mathrm{Cu^{2+}}\), and \(\mathrm{Pb^{2+}}\) removal because of its high surface area and porosity nature [14; 15; 16]. In this study, we attempted to investigate the feasibility of Mn immobilization onto BT-3 without ferrous addition, and then to develop a novel Mn-Fe binary oxide using fluidized-bed crystallization technology. The characterization of BT-3 was examined first. Then, the Mn removal efficiency in solution was detected as a function of reaction time. Furthermore, the effect of KMnO\({}_{4}\) concentration, BT-3 dosage, and initial solution \(\mathrm{pH_{i}}\) was studied through bench scale studies. The optimized parameters were also proposed. Finally, a possible mechanism to synthesize the novel Mn-Fe was rationally proposed via the kinetic study of Mn removal.
# User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks
## 6 Conclusions  
This paper focused on a ultra dense network where users are under the coverage of multiple cells, thus network performance is strongly influenced by the cell association criterion. In particular, the paper presented a new association policy, where energy efficiency is jointly considered together with communication security. Exploiting the physical layer security it is possible to define a new metric called _secure energy efficiency_  
**Author Contributions:** Conceptualization, D.M., L.M., and S.M.; methodology, D.M., L.M., and S.M.; software, D.M.; formal analysis, D.M. and L.M.; writing--original draft preparation, D.M. and L.M.; writing--review and editing, D.M., L.M., and S.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the European Union's Horizon 2020 Research and Innovation Program under Grant 872752 and in part by the European Telecommunications Standards Institute (ETSI) SmartBAN.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No data available.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 6: Percentage of association of users with different cells’ type with \(\mathcal{N}_{C}=10\) and \(\bar{\mathcal{N}}_{U}=30\).
# Service-Driven Dynamic Beam Hopping with Resource Allocation for LEO Satellites
## 4 Simulation Results and Analysis
### Performance Comparison and Analysis
Deploying the trained agents in a distributed manner in the LEO satellite communication system allows for real-time resource allocation decisions in order to meet service requirements. Our simulation results indicate that the MARL-VDN algorithm demonstrates superior performance in terms of user satisfaction and delay fairness. The MARL-VDN model, after offline training, has strong generalization ability and high execution efficiency, enabling it to flexibly handle diverse scenarios. It provides a practical and efficient solution for the intelligent management of satellite resources in dynamic service scenarios. Conceptualization, H.X. and L.L. ; methodology, H.X. and L.L. ; software, H.X. and L.L. ; validation, L.L. ; formal analysis, L.L. ; investigation, H.X. and L.L.L. ; resources, H.X. and L.L.L. ; writing--original draft, H.X. and L.L. ; writing--review and editing, L.L. and Z.Z. ; funding acquisition, L.L. and Z.Z. ; project administration, L.L. ; supervision, L.L. and Z.Z. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Key Research and Development Program of China under Grant 2022 YFB2902100, in part by the National Science Foundation of China under Grant 62401271, in part by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology (NUIST) under Grant 2023r134, and in part by the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province under Grant 24 KJB510019. Data is contained within the article. The authors declare no conflicts of interest.
###### Abstract
While various strategies may apply to the same task, some are more effective than others in particular situations. Artificial neural networks (ANN) became a popular model for dealing with data that could be considered a learning paradigm to address biomedical imaging tasks. As opposed to purely statistical models (such as logistic regression), artificial neural networks are inspired by biological neural networks of the human brain. ANNs learning properties are based on the computational properties of the neural ensemble, whose global function is the result of the coordinated activity of many smaller units, each of which performs an elementary computational operation, substantially summing the inputs--which are weighted differently--and transferring the information through the neuron when a certain threshold value is reached. Convolutional Neural Networks (CNNs) are a type of particularly complex ANN that has paved the way for DL in biomedical imaging [38; 39; 40; 41]. DL could be defined as the high-throughput extraction of information from a huge amount of data using artificial neural networks, in which multiple layers of processing are used to extract progressively higher-level higher features. DL networks have been developed in recent years as a result of the availability of larger datasets and faster computation speeds. In a DL network, each layer represents a higher level of abstraction, with the number of layers determining the model depth. Nowadays, CNNs are the most widely utilized artificial neural networks for issues involving radiological image processing and recognition and are inspired by the mammalian visual cortex [17]. In CNN, the systems consist of various sub-networks hierarchically embedded into one another. DL models can be used to find complex patterns in large data sets that go beyond the features that a radiologist could extract, such as tumor segmentation and feature extraction. Still, they do so by taking the entire image as an input. DL algorithms are well suited for imaging recognition tasks such as pattern detection and precise lesion segmentation [42].
# Detection of Volatile Constituents from Food Lures by Tephritid Fruit Flies
## Materials and Methods
### Synthetic Blends  
Based on the GC-EAD results of each of the four fruit fly species and the relative quantities found in the headspace (Table 1), five synthetic blends were formulated in paraffin oil (Merck). One blend was composed of all 13 compounds that elicited variable antennal responses in any of the fruit fly species, named \"13-blend\". The other four blends were formulated based on the response profiles of each of tephritid fruit fly species under study (_B. dorsalis_, _B. zonata_, _C. capitata_ and _Z. cucurbiae_) and named accordingly (_dorsablend_, _zonablend_, _capiblend_ and _cucublend_) (Table 1). The blends were tested in a behavioral olfactometer assay, see below. The odor blend was applied on a filter paper (Whatman(tm) Grade 1). The release rate of the compounds from the filter paper was quantified for each of the six synthetic blends from the filter paper sources and adjusted to match the ratios in the headspace using solid-phase micro extraction (SPME) on a fiber coated with (DVB/CAR/PDMS 50/30 \(\upmu\)m; Supelco, Bellefonte, PA, USA) and GC-MS analysis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Blend Components** & **Ratio** & **CAS** & **Kovats** & _B. zonata_ & _B. zonata_ & _C. capitate_ & _Z. cucurbiae_ \\ (**General Blend**) & (**ng mL\({}^{-1}\))** & & **Index** & **dorsalblend** & **zonablend** & **capiblend** & **cucublend** \\ \hline
3-Methyl-1-butanol & 35 & 123-51-3 & 763 & * & * & * & \\ Ethyl butanoate & 3 & 105-54-4 & 810 & * & * & * & \\ butyl acetate & 62 & 123-86-4 & 821 & * & * & * & * \\
2-Methylpyrazine & 4 & 109-08-0 & 831 & * & * & * \\
3-Methylbutyl acetate & 94 & 123-92-2 & 877 & * & * & * & * \\
2,5-Dimethylpyrazine & 7 & 123-32-0 & 911 & * & & * & * \\ Benzaldehyde & 1 & 100-52-7 & 963 & * & & & \\ Ethyl hexanoate & 112 & 123-66-6 & 993 & * & * & * & * \\
2,6-Dimethylpyrazine & 7 & 108-50-9 & 912 & * & * & * \\ Ethyl (E)-2-methylbut-2-enoate & 1 & 5837-78-5 & 937 & * & & * \\
2-Myrcene & 41 & 123-35-3 & 987 & * & & * \\
2-Methylophenol & 6 & 90-05-1 & 1082 & & * & * \\
1-Phenylethyl acetate & 249 & 93-92-5 & 1189 & & * & * \\ \hline \hline \end{tabular} * blend composition.  
\end{table}
Table 1: Chemical components of the four species-based and one general blend, identified by GC-EAD and GC-MS.
## 5 Precoding as Cooperative and Hybrid Networks
### Hybrid RF/VLC Networks  
Indoor VLC networks are usually deployed in scenarios where an RF communication system, e.g., WiFi, femtocells or other cellular network, are already available. Enabling cooperation among both systems leads to the concept of the hybrid VLC/RF network firstly proposed in [182]. In this sense, although VLC systems outperform the spectral efficiency of RF-based small cells [173], the cooperation between VLC and RF systems provides several benefits: enabling load balancing between VLC and RF networks, and this way, also overcoming VLC LoS-link blockages; guaranteeing users mobility by providing a seamless and robust coverage; and providing a comfortable RF uplink for VLC networks.  
The design of VLC/RF networks taking into consideration the number of optical APs and served users is studied in [183]. In [184], dynamic load balancing in a hybrid VLC/RF network is proposed. It is shown that hybrid VLC/RF networks are useful for providing high data rates in areas where the received optical power is not high enough, i.e., shadowed areas. The load balancing algorithms for hybrid VLC/RF are improved in [103], considering both fairness and data rate in order to satisfy a network quality of service. Furthermore, considering the probability of blocking (shadowing), the performance of hybrid VLC/RF networks is analyzed in [102] using game theory. Interestingly, it is shown that there exists a trade-off between FoV, probability of blocking and network performance. This way, the implementation of BIA based on the concept of reconfigurable photodetector, which provides a wide FoV comprising several PDs, in hybrid VLC/RF networks is proposed in [179].  
Beyond the application of load balancing algorithms for optimizing the performance of the network, hybrid VLC/RF systems can be considered for several purposes, and they are also classified into the multi footprint category.
## 4 Structural Damage Identification Based on SIPSO Algorithm
### Triple Damages Identification Case  
In this condition, there are triple damaged sections located at element 5, element 8, and element 17. The damage index is set as 10% and 40% and the noise level is set as 5% and 10%, separately for the two damage indexes.  
As shown in Figure 9a and Table 7, when the damage index is set as 10% and the noise level is set as 5%, the PSO algorithm can identify the damages at element 5, element 8, and element 17 except for five misjudged-as-damaged sections. In this condition, the SAPSO algorithm has three misjudged-as-damaged sections. The identification results of the SNPSO algorithm and the SIPSO algorithm are the best with only one misjudged-as-damaged section at element 11.  
Figure 9: _Cont._  
As shown in Figure 9b, when the damage index is set as 10% and the noise level is set as 10%, the PSO algorithm can identify the damages at element 5, element 8, and element 17 except for ten misjudged-as-damaged sections. In this condition, the SAPSO algorithm has four misjudged-as-damaged sections. The identification results of the SNPSO algorithm and the SIPSO algorithm are the best with only one misjudged-as-damaged section at element 11.  
As shown in Figure 9c, when the damage index is set as 40% and the noise level is set as 5%, the PSO algorithm has two misjudged-as-damaged sections, and the other three algorithms have one misjudged-as-damaged section. As shown in Figure 9d, when the damage index is set as 40% and the noise level is set as 10%, the PSO algorithm has three misjudged-as-damaged sections while the SNPSO algorithm and the SAPSO algorithm have two misjudged-as-damaged sections. In this condition, the identification result of the SIPSO algorithm is the best with only one misjudged-as-damaged section at element 2.  
Considering the numerical simulation results of the four conditions with triple damaged sections, the identification results of the PSO algorithm are the worst and those of the SIPSO algorithm are the best among the four PSO-based algorithms. When the SIPSO  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Condition** & **Damage Index** & **Noise Level** & **PSO** & **SNPSO** & **SAPSO** & **SIPSO** \\ \hline
10 & 10\% & 5\% & 5 & 1 & 3 & **1** \\
11 & 10\% & 10\% & 10 & 1 & 4 & **1** \\
12 & 40\% & 5\% & 2 & 1 & 1 & **1** \\
13 & 40\% & 10\% & 3 & 2 & 2 & **1** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Numbers of misjudgments in damage detection of four PSO-based algorithms with triple damages.  
Figure 9: Triple damages detection results of four PSO-based algorithms: (**a**) 10% damage index with 5% noise level; (**b**) 10% damage index with 10% noise level; (**c**) 40% damage index with 5% noise level; (**d**) 40% damage index with 10% noise level.  
algorithm is used for triple damage sections identification, the number of misjudgment elements is only one in all conditions of this case.
# An Energy Efficient Routing Approach for IoT Enabled Underwater WSNs in Smart Cities
## 4 Results and Discussion
### Performance Evaluation  
In the proposed study we have performed the comparative analysis with the benchmarks, which are DBR and EEDBR. To measure the performance of the proposed study, the behavior of underwater (ACH)\({}^{2}\) is equated with the DBR and EEDBR protocols. The benchmarks are still considered and utilized by the organizations to gather data from the environment. Furthermore, we have also used the extended form of LEACH, DEEC, TEEN, and SEP routing protocols to analyze the performance of each protocol underwater. All of the listed routing protocols perform according to the considered scenarios. By doing so, we have obtained the promising results, which will assist the researchers in further exploration of the routing protocols.  
For simulation, we have used MATLAB (Mathworks Inc., USA) to measure the performance of routing protocols. The list of parameters with their values are considered, as shown in Table 2 below:  
\begin{table}
\begin{tabular}{l r} \hline \hline
**Parameters** & **Values** \\ \hline Network Region & 500 m \(\times\) 500 m \(\times\) 500 m \\ Number of Nodes & 225 \\ Number of Sinks & 1, 4, 2 \\ Initial Energy & 5 joule \\ Data Rate & 10 kbps \\ Number of Rounds & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
For all of the aforementioned scenarios, as discussed in Section 1, we believe that it is mandatory to claim here that it is not possible to reproduce the same results for the next time because the nodes are deployed randomly underwater and, due to unpredictable nature of water nodes, can get scattered.
# Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry
###### Abstract  
According to the literature review performed, there are few methods focused on the study of qualitative and quantitative variables when making demand projections by using fuzzy logic and artificial neural networks. The purpose of this research is to build a hybrid method for integrating demand forecasts generated from expert judgements and historical data and application in the automotive industry. Demand forecasts through the integration of variables; expert judgements and historical data using fuzzy logic and neural network. The methodology includes the integration of expert and historical data applying the Delphi method as a means of collecting fuzzy date. The result according to proposed methodology shows how fuzzy logic and neural networks is an alternative for demand planning activity. Machine learning techniques are techniques that generate alternatives for the tools development for demand forecasting. In this study, qualitative and quantitative variables are integrated through the implementation of fuzzy logic and time series artificial neural networks. The study aims to focus in manufacturing industry factors in conjunction time series data.  
demand forecasting; machine learning; fuzzy logic; artificial neural network +
Footnote †: journal: Applied sciences
## 2 Experimental Programme
### Specim Characteristics  
In each experimental series eight NSM CFRP-concrete specimens combining two groove thicknesses (7.5 and 10.0 mm) and two bonded lengths (150 and 225 mm) were loaded under two sustained load levels (15% and 30% of the instantaneous ultimate load, \(P_{u}\)). Concrete blocks measuring 200 mm \(\times\) 200 mm \(\times\) 300 mm and 200 mm \(\times\) 200 mm \(\times\) 370 mm were used for short and long bonded length specimens, respectively. Figure 1a shows a lateral view of the setup for the single shear test and Figure 1b presents a detailed view of the groove dimensions and the CFRP strip into the groove.  
For each experimental series, the concrete blocks were cast with a standard ready-mix concrete supplied by a local company and cured for 28 days under laboratory conditions. Once the concrete had cured, 15 mm deep grooves were then cut and cleaned. The adhesive was prepared by mixing a 3:1 ratio of resin and hardener, as indicated by the manufacturer. Next, CFRP strips were carefully introduced into the grooves and then the NSM CFRP-concrete specimens were subsequently cured for 12 days under laboratory conditions before testing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Nomenclature**} & **Bonded Length,** & **Groove Thickness, \(t_{g}\)** & **Ultimate Load, \(P_{u}\)** & **Stiffness, \(K_{e}\)** & **Failure Mode** \\  & \(L_{b}\) [mm] & [mm] & [kN] & [kN/m] & \\ \hline I-150-10 & 150 & 10 & 43.9 & 172.8 & F-A \\ I-150-7.5 & 150 & 7.5 & 47.1 & 183.5 & C \\ I-225-10 & 225 & 10 & 54.7 & 175.4 & F-A \\ I-225-7.5 & 225 & 7.5 & 57.7 & 181.7 & C \\ \hline \hline \end{tabular} Note: F-A = failure in the FRP-adhesive interface; C = cohesive failure in the concrete.  
\end{table}
Table 1: Experimental results of the instantaneous pull-out tests.  
Figure 1: (**a**) lateral view of the setup and (**b**) detailed view of the groove dimensions and CFRP strip position.  
The specimens under sustained loading were called S-SL-\(L_{b}\)-\(t_{g}\)-\(\%_{0}P_{u}\), where S stands for the series number, SL for Sustained-load, \(L_{b}\) and \(t_{g}\) are the bonded length and groove thickness, respectively, and \(\%_{0}P_{u}\) the percentage of ultimate load applied as sustained load.  
The residual response of the NSM CFRP-concrete joint after SL was evaluated for all the specimens by employing an instantaneous pull-out test until failure at room temperature (20 \({}^{\circ}\)C and 55% RH). Each specimen was then called S-PSL-\(L_{b}\)-\(t_{g}\)-\(\%_{0}P_{u}\), where PSL stands for post-sustained-loading and the rest of the parameters representing the same as those previously defined above. It should be noted that the specimens from series S3-B were those from the S3-A series, therefore, only the S3-B series specimens' PSL behaviour following the sustained-loading (SL) tests could be studied.
## 2 Background and Definitions
### Unmanned Surface Vessels (USV)  
Unmanned surface vessels (USVs) and unmanned surface vehicles are autonomous vehicles capable of performing tasks in diverse and complex environments without human intervention. In maritime communications, the limitations of UAVs have prompted industry and academia to explore the potential of USVs as a competitive solution to achieving the vision of 6G networks with seamless coverage and autonomous capabilities [27]. USVs' robust, waterproof technology allows for long-term operation, even in challenging weather conditions. They possess the ability to autonomously sense and gather information from various maritime terminals, promptly identifying risks and responding accordingly. These USVs are dispatched to periodically collect maritime data from buoys and sensor nodes across vast areas. Equipped with high-gain antennas and computation units, USVs are well-prepared for future mobile communications in computing and caching-enabled networks. They can perform local computations or offload tasks to satellites or base stations as needed [11]. USVs offer numerous advantages for future maritime wireless communication systems. They can provide flexible wireless communication services to remote maritime devices, such as ships, buoys, beacons, sensors, and platforms, without the need for dedicated high-gain antennas or satellite infrastructure. This enables the expansion of communication coverage beyond terrestrial domains, facilitating commercial and military applications such as deep-sea sightseeing and broad-sea monitoring. They can undertake longer and more hazardous missions, incur lower maintenance costs, ensure greater personnel safety due to their unmanned nature, and possess enhanced maneuverability and deployabilityin shallow waters where larger craft might struggle. Additionally, USVs have the potential for enabling deeper water depth monitoring and sampling compared to aircraft/UAVs and spacecraft [28].