esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Two factors central composite design
#### 3.1.3 Effect of Dispersive Solvents  
In dispersive LLME, a dispersive solvent such as methanol, acetone, or acetonitrile with high miscibility in both extractant and aqueous phases can give rise to the formation of small droplets throughout the aqueous sample. The extraction time can be shortened because of the increased surface area between the extractant and aqueous sample in the cloudy solution, so the equilibrium is achieved quickly [20]. The extraction efficiencies by LLME-SPME with 20 \(\upmu\)L hexane and 500 \(\upmu\)L of different dispersive solvents including methanol, acetone, and acetonitrile were compared and the results are reported in Figure 4A. None of the dispersive solvents improved the extraction efficiency. The volume effect of acetonitrile as a dispersive solvent can be seen in Figure 4B. There was no significant difference for extraction efficiency when using 0, 100, 200, or 500 \(\upmu\)L acetonitrile (\(p\)-value of 0.2 by one-way analysis of variance).  
Different extraction times in the range of 5-90 min for SPME with 20 \(\upmu\)L hexane or with 20 \(\upmu\)L hexane and 100 \(\upmu\)L acetonitrile were evaluated (Figure 4C). The maximum response was achieved at 60 min for LLME-SPME with hexane, and at 30 min for LLME-SPME with hexane and acetonitrile. No advantage to using acetonitrile was achieved, especially at the pre-selected extraction time of 15 min.  
Figure 3: Response surface of the second-order polynomial model with the zoom-in window of interested region (**A**) and response surface model at 15 min (**B**).
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 9 Conclusions
### Future Perspectives and Open Challenges  
We observed that the propagation models in the literature investigated and employed in the simulations of the current paper have limitations that render them not applicable in all real-life scenarios. Consequently, we intend to explore new channel models to reach beyond the limits of the propagation models employed until now.  
The proposed algorithm has advantages and limitations. An advantage of the algorithm consists of the scalability in terms of the propagation models, UVs initial positions and other parameters. Consequently, further, we would like to implement more complex propagation models. In the future, we will enhance the algorithm to consider the impact of the interference experienced by UAVs at low altitude [82]. Based on the proposed algorithm and the implementation of collision avoidance techniques [83], an autopilot system can be further enhanced.  
A drawback of the algorithm, which will be mitigated in future, consists of the high execution time for technical solutions involving a higher link budget.  
Finally, we will perform thorough measurement campaigns and define propagation loss models based on on-field experimental measurements.  
Conceptualisation, A.-M.D., S.H. and C.Z.; methodology, A.-M.D. and S.H.; software, A.-M.D.; validation, A.-M.D., S.H. and C.Z.; formal analysis, A.-M.D. and S.H.; investigation, A.-M.D., S.H. and C.Z.; resources, A.-M.D., S.H. and C.Z.; data curation, A.-M.D.; writing--original draft preparation, A.-M.D., S.H. and C.Z.; writing--review and editing, A.-M.D., S.H. and C.Z.; visualisation, A.-M.D. and S.H.; supervision, A.-M.D. and S.H.; funding acquisition, A.-M.D. and S.H. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results has received funding from the NO Grants 2014-2021, under Project contract no. 42/2021, RO-NO-2019-0499 -\"A Massive MIMO Enabled IoT Platform with Networking Slicing for Beyond 5G IoV/V2X and Maritime Services\"-SOLID-B5G and was supported by a grant of the Romanian Ministry of Education and Research, CCCDI-UEFSCDI, project number PN-III-P2-2.1-PED-2019-1945 (NGI-UAV-AGRO), within PNCDI III.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Agent-Based Virtual Machine Migration for Load Balancing and Co-Resident Attack in Cloud Computing
## 5 Agent-Based VM Migration  
### Agent-Based VM Migration for Load-Balancing and Co-Resident Attacks (ALBA)  
ALBA is a mechanism with two distinct roles: initiator and participant. The initiator role is triggered by an SA whose resource usage surpasses the threshold or by an SA with a  
Figure 2: Agent-based VM application, allocation and migration architecture.  
VM that does not match the security level, which then initiates a VM migration. The SA broadcasts a call for proposals to participant SAs within the same cloud computing unit (Figure 3), containing the VM's description that the initiator is attempting to migrate. The participant SAs which have the same security level as the VM's description respond with a proposal message, including their current resource utilization statistics, indicating that they can host the VM without breaching their migration thresholds or security level.  
The initiator SA analyzes the received proposed messages from the participating SAs with specified algorithm. The resource utilization data are reviewed. If the initiator SA employs only the CPU-based migration threshold, the participant SA with the highest idle CPU resource is chosen. Similarly, if the initiator SA relies solely on the memory-based migration threshold, the participant SA with the highest memory resource is chosen. Furthermore, if the initiator SA employs both the CPU-based and memory-based migration criteria simultaneously, the participant SA with the highest CPU and memory resource is selected.  
Once the most suitable participant SA is selected, the initiator SA migrates the VM to the chosen participant SA (Figure 3). In the event that the initiator SA receives no accept proposals, i.e., no participant SA has sufficient CPU and memory resources, or a matching security level, the initiator SA waits for a defined time interval and then resends the proposal to the participants (Figure 3).  
Figure 3: Agent-based VM migration for load-balancing and co-resident attacks (ALBA).
## 3 Materials and Methods
### YOLO-Ship Network  
Targeting the characteristics of small samples, diversity, and stacking occlusion in ship component recognition, YOLOv8n was selected as the base network. Compared to newer YOLO versions, YOLOv8 achieves better balance between accuracy and computational efficiency, with good maturity and stability in industrial applications [17], making it more suitable for the lightweight objectives of this research.  
Based on the aforementioned TSA triplet spatial reconstruction attention mechanism, the TSA mechanism is combined with the C2f module for improvement, constructing the C2f-TSA module with the structure shown in Figure 4.  
The improved C2f-TSA module replaces the original C2f modules in the Neck section of the YOLOv8n network, forming the YOLO-Ship detection network. Through the spatial reconstruction and cross-dimensional feature fusion capabilities of the TSA mechanism, it better handles small target detection and stacking occlusion problems in ship component images. Compared to the original network, the lightweight design of TSA ensures that the improved network reduces model parameters while improving convergence speed and detection accuracy, meeting the real-time requirements of industrial applications. The complete structure of the YOLO-Ship network is shown in Figure 5.  
Figure 4: C2f-TSA structure diagram.
## 4 Discussion
We believe that although no statistical differences are shown, if the current conditions are maintained for more days, the fish at higher stocking densities could be experiencing greater stress, and this could be reflected statistically, demonstrating that this is part of our current studies. Cortisol is released as an indicator of chronic and acute stress, which elevates energy expenditure and, therefore, stored somatic energy expenditure also subsequently increases, which could ultimately cause fish growth retardation [37; 46]. On the other hand, the results observed in hct, total proteins, and albumins did not show significant differences and are within the reference ranges proposed by [PERSON] et al. (2013). In addition, the hct corresponds to that of benthic and sedentary species such as _D. latifrons_([PERSON] et al., 2017). This reinforces the idea that the effects observed in the above parameters are the result of exposure to chronic stress due to stocking density and not induced by a deficiency in the diet supplied. As for the chart plants, the final total weight was significantly higher in the T3 treatment (9.01 \(\pm\) 3.84 g) compared to the rest of the treatments. This shows that high fish densities benefit plant growth in aquaponic systems. Despite the differences in stocking density, ammonium levels remained within tolerable limits for _D. latifrons_([PERSON] et al., 2017). However, the highest stocking density treatment (T3) exhibited an increase in ammonium and nitrite levels, which could have contributed to the stress observed in the fish, as reflected by elevated cortisol levels and decreased globulin concentrations in this treatment. Conversely, the higher nitrate levels in T3 might explain the increased plant growth, as this compound serves as an essential nitrogen source for their development ([PERSON] et al., 2017). This is consistent with previous research on aquaponic systems, which demonstrated that higher fish stocking densities can enhance plant yields. [PERSON] et al. (2019) reported that the yield of water spinach (_Ipomoea aquatica_) increased significantly with increasing tilapia (_O. niloticus_) stocking density. Similarly, [PERSON] et al. (2019) mentioned that the highest yield of gotu kola _Centella asiatica_ was obtained in the highest stocking density treatment of koi carp (C. _carpio_ var. _Koi_). In the case of lettuce (_Lactuca sativa_), [PERSON] et al. (2019) commented that better yield parameters were obtained by increasing the culture density of _O. niloticus_ L. Other researchers reported better yields in basil (_Ocimum basilicum_ L.) when the culture density of basa (_Pangasianodon hypophthalmus_) was increased ([PERSON] et al., 2017) (Table 1). However, it is important to note that other plant growth parameters, such as root-to-shoot ratio, did not show significant differences among treatments, indicating that further studies are needed to fully understand fish-plant interactions in these systems.
## 3 Experimental Results and Discussion
### Training and Evaluation of DCNNs
#### 3.2.3 AlexNet Classification Results  
The training performance of AlexNet for 30 epochs, with and without noisy datasets, is shown in Figure 13.  
Figure 12: VGG-16 confusion matrices for noisy and noiseless testing datasets.  
Figure 13: AlexNet training performance for noisy and noiseless datasets.  
The performance trend of AlexNet is in line with the ResNet-50 and VGG-16 models. It obtained maximum training and validation accuracies of 93.92% and 92.19%, respectively, with 0.205% training and 0.269%, validation losses. The training and validation accuracies decreased to 92.50% and 90.938%, respectively, for 20 dB noise. The confusion matrices for noisy and noiseless datasets are illustrated in Figure 14. The test accuracy is relatively high for noiseless datasets as compared to other noisy datasets. Moreover, the performance matrices including accuracy, precision, recall, and F1-score are given in Table 4. The results specify that AlexNet attains the highest performance for the noiseless test dataset, giving an average value of accuracy (99.38%), precision (95.21%), recall (95%), and F1-score (95.01%). In the case of noisy test datasets, it produces the lowest average results of accuracy (99.91%), precision (91.73%), recall (91.25%), and F1-score (91.23%) with 20 dB noise, as shown in Table 4.
## 2 Materials and Methods
### Life Cycle Inventory
#### 2.2.2.1 Vehicle Use Phase  
The emissions and consumptions of a vehicle depend to a great extent on the vehicle typology and the driving mission. In this study, the tailpipe emissions and fuel consumptions have been evaluated using an ad hoc developed backward-facing simulation approach [35] that takes the longitudinal dynamics of the case-study LDCV into account and estimates the efficiency of the diesel ICE, electric machines, the power converter, and transmission components on the basis of experimentally derived look-up tables provided by the manufacturers. Using such an approach, the ZEBRA and NMC batteries were modelled by means of an equivalent resistance circuit, in which the resistance and the open-circuit voltage of the battery were SOC-dependent, on the basis of experimental data provided by the manufactures. This approach was also used to estimate the CO\({}_{2}\) and pollutant emissions for the diesel LDCV [35].  
A conventional version of an LDCV and two electrified versions, equipped with NMC111 and sodium-ion ZEBRA batteries, respectively, have been studied. The vehicle lifespan considered in all the simulations was 240,000 kilometres. The main characteristics of the considered LDCVs are presented in Table 3. The different kerb weights for the two electric vehicles are related to the different chemistries of the batteries. ZEBRA-BEV was equipped with two battery packs [35]. A two-battery pack configuration was selected for the NMC-BEV in order to have a total battery capacity as close as possible to that of the ZEBRA vehicle. An additional configuration of NMC-BEV with three battery packs was also considered, and the results are presented in the result section. This configuration is characterised by a larger total battery capacity (105 kWh), i.e., an extended driving range and a slightly higher kerb mass (3185 kg) than the ZEBRA vehicle.  
The driving cycle was chosen to represent a typical urban delivery mission of the LDCV. First, the LDCV was driven along an AUDC and then along an URBAN driving cycle with 50% of its maximum payload [35]. The AUDC + URBAN driving cycles were then repeated with no payload. This mission is called M1 in this paper. The AUDC and URBAN driving cycles in Table 4 are characterised by means of suitable metrics: duration, length, average (V-tract) and maximum speed (V-max). In addition, the table also reports the main energy performance of the LDCV for each driving cycle in running order: Specific Energy Consumption (SEC) for both traction (tract) and braking (brake), as well as the average power required for traction at the wheel level (P-tract).  
The NMC111 battery has a lower energy consumption value than the ZEBRA ones. This can be ascribed to the fact that the ZEBRA battery requires an internal temperature of 270 \({}^{\circ}\)C to 350 \({}^{\circ}\)C for correct operation [35]. This requirement introduces a thermal flow through the battery when it is not working, which partially decreases the state of charge of the battery.
# Design of a Rotary Transformer for Installations on Large Shafts
## 3 Finite-Elements Analysis  
Electromagnetic analyses with finite-elements software were set in view of the optimization of the transformer design. Both 2D and 3D runs were needed. In particular, 2D analyses were performed with the free FEMM software and they allowed preliminary investigations on the device for checking its performance as well as the sizing of its magnetic core. Furthermore, 3D analyses were carried out in ANSYS Maxwell to verify the results of the preliminary analyses and to investigate possible solutions aimed at weight reduction. In both cases, the core was supposed to be made of ferrite with relative permeability \(\mu_{r}=2100\) and a density of \(\rho=4800\,\text{kg}/\,\text{m}^{3}\). Moreover, it was observed that commercial ferrites with  
Figure 5: Equivalent circuit of the rotary transformer supplied by a full-bridge converter. For the sake of simplicity, no compensation is reported in this picture.  
similar characteristics saturate for magnetic flux density equal to \(B=0.4\) T. Furthermore, the coils were realized in copper with a pessimistic filling factor \(k=0.4\).
# A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump
## 2 Methodology
### Development of the Health Degradation Trends
#### 2.2.1 Statistical Feature Extraction  
Besides the impellers, the slurry pump also consisted of many other critical components, such as vanes, casing, etc. These components were generating various vibrating frequencies; therefore, a low-pass filter algorithm was used for specifically capturing the impellers vibrations. A cut-off frequency of 110 Hz was applied to process the slurry pump's original vibration data \(X_{k}(n)\). Then, nine traditional statistical features, i.e., mean (\(\mu\)), standard deviation (\(\sigma\)), root mean square (\(RMS\)), skewness (\(SK\)), kurtosis (\(KUI\)), crest factor (\(CF\)), clearance factor (\(CLF\)), shape factor (\(SF\)), and impulse factor (\(IF\)) in the time domain, were extracted. Subsequently, the same nine statistical features in the frequency domain were also extracted from the valid vibration signals \(X_{k}\). Table S1 (Supplementary Materials) shows the mathematical equations of the selected statistical features. The feature matrices in time and frequency domains were constructed as  
\[F_{LP}(t)=[\mu,\ \sigma,\ \text{RMS},\ \text{SK},\ \text{KU},\ \text{CF},\ \text{CLF},\ \text{SF},\ \text{IF}] \tag{4}\]  
\[F_{LP}(f)=[\mu,\ \sigma,\ \text{RMS},\ \text{SK},\ \text{KU},\ \text{CF},\ \text{CLF},\ \text{SF},\ \text{IF}] \tag{5}\]  
where  
\(N=\) data length of the dataset  
\(n=\) selected length of the considered dataset i.e., \(n=\) 1,2,..., \(N\)  
\(F_{LP}(t)=\) the features in the time domain  
\(F_{LP}(f)=\) the features in the frequency domain  
\(LP=\) low pass filtering
# Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks
## 6 Conclusions  
In this paper, we propose smart preliminary channel access (Smart PCA) for serving real-time traffic in Wi-Fi networks. We compare the developed method with two previously proposed: PCA and limiting the time for which STAs can occupy the channel for non-urgent transmissions. The comparison takes into account both the QoS for RTA traffic and the channel usage efficiency for STAs with delay-insensitive traffic.  
We show through simulations that the Smart PCA can provide significantly lower delay than the PCA in the presence of multiple RTA STAs in the network. The Smart PCA method proposed in this paper is the most effective in the case of strict requirements for maximum delay (<3 ms) and low intensity of RTA traffic. At the same time, an increase in the deviation of the data arrival time reduces the efficiency of the Smart PCA, so this method should be used only in the case of predictable RTA traffic.  
Conceptualization, D.B., E.K., and A.L.; formal analysis, D.B. and E.K.; investigation, K.C. and D.B.; methodology, E.K. and A.L.; software, K.C. and D.B.; validation, E.K. and A.L.; writing--original draft, K.C.; writing--review and editing, D.B., E.K., and A.L. All authors read and agreed to the published version of the manuscript.  
The research has been carried out at IITP RAS and supported by the Russian Science Foundation (Grant No 21-19-00846, [[https://rscf.ru/en/project/21-19-00846/](https://rscf.ru/en/project/21-19-00846/)]([https://rscf.ru/en/project/21-19-00846/](https://rscf.ru/en/project/21-19-00846/)) (accessed on 8 September 2022)).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# A Survey on Botnets: Incentives, Evolution, Detection and Current Trends
## 6 Detection and Mitigation
### Detection Mechanisms--Techniques
#### 6.1.6 Mobile Botnets  
For the purpose of clarification, Table 8 below details a number of papers that goes over mobile botnet based detection:  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Technique** & **Advantage(s)** & **Disadvantage(s)** & **Detection Rate** & **Papers** \\ \hline Risk factor based on multi-category features & High accuracy in botnet apps. Generates a pattern for Android botnet detection. & Only for static analysis. No response mechanism. & Reference [194]: 93.1\%. & [18,194] \\ \hline Dynamic real-time analysis & N/A. & Limited analysis for risk factor. & N/A. & [19] \\ \hline Machine learning performance. & General high detection performance. & Bad detection performance when the bot coexists with other applications that communicate with many hosts. & Achieves 0.93 of the FP-measure score by using graphlets of TCP and UDP with 10% of total traffic in 3-minute duration [195]. & [196] \\ \hline Application monitoring & Mitigation: user warnings if something is suspicious. & SMS and social network applications are not monitored. & N/A. & [197] \\ \hline \end{tabular}
\end{table}
Table 8: Papers describing detection of botnets in mobile devices. Each column describes the overall technique, known advantages, disadvantages, detection rate and related papers, respectively.  
Another point of interest in botnet detection is the detection of smartphone-based botnets. Some papers, such as [PERSON] and [PERSON] [18], propose assessing the potential risk of malicious apps by evaluating the API calls used by given apps. Apps shown to behave more like botnets are categorised as higher-risk and might potentially be blocked. Reference [18] also attempts to evaluate apps based on risk factors, weighing botnet-behaving apps as higher-risk compared to more benign apps. Reference [19] compares an app's permissions with a list of known harmful permissions and creates a threat level hierarchy on the basis of said permissions. Reference [194] extends the approach used in [19] by detecting botnets on the basis of both permissions and also the used API calls of each app. Reference [195] proposes the use of a graphlet-based machine learning algorithm on smartphone communication and then executing principal components analysis to identify P2P botnets on smartphones. Other approaches such as [196] run as an active agent on the smartphone OS to capture run-time data. The data is then labelled using a machine learning algorithm to determine whether or not an app acts like a botnet. Reference [197] instead asks the user to specify trusted apps and what permissions a given app should have according to the user. Any apps performing unauthorised or suspicious actions are flagged, and the user is informed. Periodic scans are performed to identify new threats and inform the user of unused apps.