esa-sceva/satcom-chunk-collection huggingface dataset:
# Synthesis of Star Nanoparticles and Their Applications for Bioactive Compound Encapsulation
## 2 SNP Preparation Method
### Ultrasound with or without Acid Hydrolysis  
This method consists of a precipitation process that involves the addition of a solution in which the starch is diluted into an aqueous phase in the presence of ultrasound homogenization that reduces the size of the particles formed due to the breaking of the starch covalent bonds as a result of the shear forces or the mechanical effects produced by the collapse of microbubbles that form sound waves [57; 58; 59].  
However, this method has been applied together with acid hydrolysis since the combination of physical and chemical processes produces nanoparticles with more desired properties [60]. This technique is simple and convenient in terms of safety, cost and can give better yield with desired particle size.  
Acid hydrolysis is a process that can be conveniently divided into three stages based on the reaction rates involved: rapid stage, slow stage, and very slow stage [61]. A series of steps is typically required to produce SNPs using the acid hydrolysis process: (i) the starch is mixed with the acid; (ii) this mixture is heated for a certain time with constant stirring; (iii) the resulting suspension is washed with distilled water by successive centrifugation until  
Figure 7: Synthesis of SNPs through ball milling method and SEM micrographs of (**a**) water chestnut SNPs [53] (reprinted with permission from Elsevier. Copyright (2019)) and (**b**) lotus stem, horse and water chestnut [54] (reprinted with permission from Elsevier. Copyright (2021)).  
neutralization is achieved and (iv) the suspension is refrigated for a specified time [62]. The main disadvantages of this method are the low recovery yield, slow reaction rate, and tendency for particle aggregation to occur [63].  
Both processes, with and without acid hydrolysis are presented in Figure 8a,b respectively.  
In 2019, [PERSON] et al. reported the synthesis of SNP from potato starch using an ultrasound-assisted method with and without acid hydrolysis. The ultrasound-assisted acid hydrolyzed starch exfoliates the native starch and modifies the structural arrangement, in this acid treatment, the amorphous nature of the starch becomes crystalline. A solution of corn and potato starch was prepared and sonicated and subsequently treated with dilute sulfuric acid. The hydrolysis of those of the SNPs was carried out by diluting part of the starch solution in distilled water and subsequently concentrated H2 SO4 was added dropwise and sonicated until the addition of dilute sulfuric acid was completed. To neutralize this solution, a solution of NaOH and distilled water was added. Finally, particle sizes of 42 nm were obtained when the acid hydrolyzed ultrasound approach was used while sizes of 80 nm were obtained when only the ultrasound was used [64].
## 3 Results  
The processes involved in the transport and deposition of pollutants in polar regions are complex and still require additional investigations. Although heavy metals or persistent organic pollutants [5; 6] have been used as proxies of environmental change, nowadays, emerging contaminants represent an additional issue of concern [2; 10]. In Antarctica, it has already been observed that contamination can derive from both local sources and long-range transport. Previous studies highlighted the presence of metals, namely, Cd, Cr, Cu, Pb, and Zn, in particulate matter [7; 36] and microplastics [37] related to the activities of the research stations in Antarctica, with wastewater, heating systems, and outdoor clothing or equipment being the major sources of contamination [7; 37; 38]. However, additional inputs derive from long-range transport. In this study, five samples of Antarctic snow collected in Terra Nova Bay were analyzed to investigate the presence of small micro- and nanomaterials, exploiting the capabilities of both TEM/EDX and Raman micro-spectroscopy. In particular, four samples were collected in areas neighboring the Italian research station Mario Zucchelli, located in the Ross Sea area in Victoria Land, and one sample in Cape Phillips, 250 km NE from the station.
# Ensemble Method of Triple Naive Bayes for Plastic Type Prediction in Sorting System Automation
###### Abstract  
Recycling has been acknowledged as a viable alternative for the management of plastic refuse. An automatic sorting system is required by the industry to predict the plastic waste based on the type before it is recycled. The plastic sorting system automation requires intelligent computing as a software system that can predict the type of plastic accurately. The ensemble method is a method that combines several single prediction methods based on machine learning into an algorithm to obtain better performance. This study aims to build intelligent computing for the automation of digital image-based plastic waste sorting systems using an ensemble method built from three naive Bayes single prediction methods. The three single models consist of one Naive Bayes (NB) model with crisp discretization and two NB models with fuzzy discretization, namely those using a combination of linear-triangular fuzzy membership functions and a combination of linear-trapezoidal fuzzy membership functions. We hypothesize that the performance of each single model and the proposed ensemble model is different, and the performance of the ensemble model is higher than all the single models used to build it. The hypothesis is proven, and there is an increase in performance from each single method to the ensemble method ranging from 2.06% to 5.56%. The evidence of this hypothesis also shows that the performance of the proposed prediction model using the ensemble method built from three naive Bayes models is high and robust.  
ensemble; crisp and fuzzy discretization; naive Bayes; plastic type prediction; sorting system automation
# Synchronizing TSN Devices via 802.1 AS over 5G Networks
## 4 Simulation Setup for TSN Synchronization over 5G Scenarios
### Simulation Scenarios  
Using the updated models, two simulation scenarios are created, starting with a simple TSN network consisting of two TSN switches and a Grand Master (GM). The GM, called the TSN Master, is connected to the first TSN switch (based on the 5G-TSN device model), which is in turn connected to the second TSN switch (also based on the 5G-TSN device model). The link between both switches is simulated by a 5G network.  
Each TSN device in the simulation has its own clock based on the Constant Drift Oscillator model provided by the INET framework. INET has three Oscillator models: Ideal Oscillator, generating ticks periodically with constant length, Constant Drift Oscillator, generating variable ticks whose length is proportional to the elapsed simulation time, and Random Drift Oscillator, generating clock drift with the random walk process. All of these Oscillator models generate ticks, with tick length varying as mentioned before, which are counted by the clock model to compute the time for that specific clock module.  
For TSN, especially for Time Synchronization simulations, Constant Drift Oscillator provides the closest oscillator behavior to the real-world scenarios since the clock either ticks a bit faster or a bit slower than the actual time. Therefore, both simulation scenarios presented in the paper add a Constant Drift Oscillator for the internal clocks of all TSN devices. The TSN Master clock oscillator also contains the drift; thus, the slaves only correct their internal clock drifts with synchronization.
# Numerical Simulation of the Hydrodynamic Performance and Self-Propulsion of a UUV near the Seabed
## 5 Conclusions  
The variation trend of hydrodynamic performance of bare UUV and self-propelled UUV with \(Hd\) and \(Re_{L}\) is similar. The coefficient of drag decreases with the increase of \(Hd\) and finally tends to be stable. The absolute value of lift coefficient decreases with the increase of \(Hd\) and finally tends to be zeros. The coefficient of pitching moment decreases with the increase of \(Hd\) and finally tends to be zeros. The hydrodynamic coefficients of UUV have a significant rate of variation near the seabed. The drag coefficient decreases with increasing \(Re_{L}\). The absolute value of the lift coefficient increases with increasing \(Re_{L}\). The pitching moment coefficient changes in the opposite trend of the lift coefficient. The drag coefficient of the self-propelled UUV increases by 16.74-17.88%. Additionally, lift coefficient, pitching moment coefficient of the self-propelled UUV increase by 69-130% and 100-300%, respectively, compared with that of bare UUV when UUV near the seabed. Thrust reduction, hull efficiency, wake fraction and rotational efficiency are weakly correlated with \(Hd\). It is found that the thrust reduction and rotational efficiency increase with increasing \(Re_{L}\), and the hull efficiency and associated flow fraction decrease with increasing \(Re_{L}\).  
The case of UUV for straight sailing were discussed for the paper. In the future, it is necessary to study the effect of seabed on the hydrodynamic performance of the self-propelled UUV with variations of the angle of attack.  
Conceptualization, Z.M., Y.H. and W.T.; validation, X.L.; formal analysis, W.T. and X.L.; data curation, X.L.; writing--original draft preparation, X.L.; writing--review and editing, W.T.; funding acquisition, Z.M. and W.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Program No. 51809214) and the Fundamental Research Funds for the Central Universities for Wenlong Tian.  
Not applicable.  
The authors declare no conflict of interest.
# Mobile Charging Scheduling Approach for Wireless
## 3 System Model and Problem Formulation
### Problem Formulation
(5) Policy. When the test environment is terminated, all the charging actions performed by the MC during the charging process will compose a charging policy, described with \(a=\Pi(S)\), where \(\Pi=\{\Lambda_{1},\ldots,\Lambda_{k},\ldots,\Lambda_{K}\}\), and \(\Lambda_{k}=(\theta_{k},\xi_{k})\). Let \(a_{\theta}(k)=\theta_{k}\) and \(a_{\zeta}(k)=\xi_{k}\), and \(\Pi:S\mapsto P(\mathcal{A})\), where \(P(\mathcal{A})\in[0,1]\). In particular, the main purpose of MC is to obtain an optimal charging policy to maximize the cumulative reward. A scheduling example of JSSTC is shown in Figure 4. A charging trajectory can be described with \(\{s(0),a(0),\ldots,s(k),a(k),\ldots,s(K-1),a(K-1),s(K)\}\), and the discounted cumulated reward is obtained by  
\[R(\Pi)=\sum_{k=0}^{K}\gamma^{k}\cdot r(s(k),a(k)), \tag{8}\]  
where \(s(k)\in S\), \(a(k)\in\mathcal{A}\). Figure 3: The action description of JSSTC. To effectively shorten the training time and teach the agent to avoid the meaningless exploration, some constraints are given as follows:  
* The MC can visit any position in the network when its remaining energy of MC is greater than \(E_{MC}^{min}\) and when its remaining energy reaches \(E_{MC}^{min}\), it will terminate the current charging operation and return the charging service station to replace the battery;
* All sensors with charging demand greater than 0 can be selected as the next charging destination;
* If the remaining energy of the sensors is 0, it will not be accessed by MC;
* Two adjacent time steps cannot choose the same charging destination.
# Novel Internet of Things Platform for In-Building Power Quality Submetering
## 2 IoT Power Quality Sensor
### Communication Technology  
The ESP8266 is used as a Wi-Fi adapter with built-in antenna to provide Wi-Fi capabilities to the MCU. This module is a self-contained SoC with integrated TCP/IP protocol stack that can give ATmega328 microcontroller access to a Wi-Fi network [42]. This module supports 802.11 b/g/n and has Wi-Fi direct along with capability for a software enabled access point. This is a low-power device specifically designed for mobile devices, wearable electronics and networking applications. Communication standards as SPI and Universal Asynchronous Receiver-Transmitter (UART) are used for communication with the module. This Wi-Fi module is also very cost effective, making it ideal to use and implement on a large scale. ESP8266 has been employed because of its ready availability in the market as well as its economic advantage. ESP8266 has been configured to continuously send sensor data to an open-source cloud platform via the Wi-Fi module.
# The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction
The principal component analysis method of interval data has been widely used in sensor fault diagnosis [28; 29; 30], spacecraft fault diagnosis [31], and other fault diagnosis fields. This paper introduces CIPCA into the UAV fault prediction and uses a back-propagation neural network (BPNN) to construct the CIPCA-BPNN fault prediction model. For the purpose of failure prediction, we used real labeled UAV flight data and selected flight data 30 s before the fault as fault data. The rest of this paper is organized as follows. Section 2 describes the interval data and CIPCA. Section 3 discusses the application process of CIPCA in UAV failure prediction. Section 4 describes the experiment in this paper. Section 5 analyzes the experimental results. In Section 6, the conclusions are given.
## 4 Conclusions  
The present work focuses on an ANN to analyze a Savonius-type VAWT. The reason is the current necessity to employ renewable resources to reduce both global pollution and the dependency on finite resources such as fossil fuels. The Savonius turbine is appropriate for small power generations, but its main disadvantage relays on its low efficiency. With a proper design, it is possible to increment its efficiency. According to this, the present work proposes a bio-inspired blade profile based on the Fibonacci spiral. Previous studies showed that the performance of these turbines could be affected by several parameters. The present work focuses on the aspect ratio, overlap, and twist angle that influence the efficiency of the Savonius turbine. The variety of possible combinations of these parameters is immense; moreover, their relation to efficiency and thus optimization is a complex nonlinear problem. To predict the efficiency, an ANN was developed. The data employed to set the ANN were obtained through a validated CFD model. One hundred twenty-five cases were employed to train, validate, and test the neural network. The ANN was validated using R-value, which was close to 1. This shows that the ANN can accurately predict the performance as an alternative to classical modeling techniques and direct measurements. Once established, the ANN was used to analyze a total of 793,881 cases using different combinations of aspect ratios, overlap/chord length ratios, and twist angles. It was found that the most appropriate combination corresponds to aspect ratio 7.5, overlap/chord length ratio 0.1125, and twist angle 112\({}^{\circ}\). This combination improves the maximum power coefficient by 32.4% in comparison to the base case with aspect ratio 1, overlap/chord length ratio 0, and twist angle 0. Regarding future works, it will be interesting to analyze other improvements such as deflectors, several stages, and combinations of Savonius-Darrieus stages.  
Conceptualization, J.d.D.R.G., J.B.D., J.T.M., A.C.C., M.I.L.G. and C.G.C.; methodology, J.d.D.R.G., J.B.D., J.T.M., A.C.C., M.I.L.G. and C.G.C.; software, J.d.D.R.G., J.B.D., J.T.M., A.C.C., M.I.L.G. and C.G.C.; validation, J.d.D.R.G., J.B.D., J.T.M., A.C.C., M.I.L.G. and C.G.C.; formalism analysis, J.d.D.R.G., J.B.D., J.T.M., A.C.C., M.I.L.G. and C.G.C.; investigations, J.d.D.R.G., J.B.D., J.T.M., A.C.C., M.I.L.G. and C.G.C.;; writing--original draft preparation, J.d.D.R.G., J.B.D., J.T.M., A.C.C., M.I.L.G. and C.G.C.; writing--review and editing, J.d.D.R.G., J.B.D., J.T.M., A.C.C., M.I.L.G. and C.G.C.; supervision, J.d.D.R.G., J.B.D., J.T.M., A.C.C., M.I.L.G. and C.G.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Implementation and Discussion
### Limitation and Future Work
#### 4.3.3 The Inclusion of Critical Aspects of Medical Decision-Making  
Collaborative medical decision-making is a hard nut to crack [1]. Economical and psychological effects can never be ignored. Similarly, the participant's must be well aware of other entities' knowledge and capacity. Emergency and context-aware decision assessment are not considered at this moment. In the future, the study plans to include the aforementioned minor factors in the PoF consensus gathering algorithm. As the experimental evaluation and comparison with other studies are ongoing, minor factors were overlooked in this version of the study.
# A Lightweight Fault Diagnosis with Domain Adaptation for Defected Bearings
## 2 Methodology
### Backbone Design  
#### 2.2.1 Time-Frequency Analysis Using S-Transform  
Vibration signals are widely used in fault diagnosis due to their sensitivity to mechanical anomalies. The signals are typically collected using accelerometers mounted on the bearing housing. The sampling frequency should be chosen carefully to ensure that the relevant frequency components of the bearing faults are captured. For instance, a sampling frequency of at least 10 kHz is often recommended to capture high-frequency components associated with localized defects.  
Once the raw vibration signals are acquired, the time-frequency representation using the S-transform is imported. The S-transform is a powerful time-frequency analysis tool that combines the advantages of the Short-Time Fourier Transform (STFT) and the Wavelet Transform. It provides a time-frequency representation that is particularly useful for analyzing non-stationary signals. The S-transform is defined as follows:  
\[S(\tau,f)=\int_{-\infty}^{\infty}x(t)\frac{|f|}{\sqrt{2\pi}}e^{-\frac{(\tau-t) ^{2}/2}{2}}e^{-l2\pi ft}dt \tag{1}\]  
where \(x(t)\) is the vibration signal, \(\tau\) is the time shift, and \(f\) is the frequency. The S-transform produces a time-frequency matrix that captures the frequency content of the signal. Then, it is used to generate a time-frequency image, which serves as the input to the deep learning model.  
The time-frequency image provides a rich representation of the vibration signal, highlighting the presence of fault-related frequency components and their temporal evolution. This representation is particularly useful for detecting and diagnosing bearing faults, as it allows the model to capture both the frequency and temporal characteristics of the fault.
## 4 Experiments
### Best Model Combination for Highest Transferability  
To identify the most effective combination of models for optimizing adversarial textures with high transferability, we experimented with various combinations of YOLOv8, YOLOv5, and YOLOv3 models. Table 7 presents the [EMAIL_ADDRESS] performance of these adversarial textures across a diverse set of object detection architectures, including one-stage, two-stage, and transformer-based models.  
Figure 5 presents images of the rendered truck with various textures optimized using different model combinations. The resulting textures demonstrate both recurring structural patterns and a diverse color palette, highlighting the extensive solution space for adversarial textures. This diversity suggests the possibility of a texture sample within this solution space that could be universally effective in deceiving a wide range of object detection models; however, further research is needed to explore and validate this potential.  
Figure 6 provides a bar chart summarizing the average [EMAIL_ADDRESS] scores for each texture across these model categories. Our findings showed that optimizing adversarial textures on a combination of models from different architectures and sizes yielded the best transferability. Specifically, the texture optimized on the combination of YOLOv8n, YOLOv5m, and YOLOv3 achieved the lowest average [EMAIL_ADDRESS] of 0.0972 across all tested models. This is more than 0.11 less than single-model textures (0.1148 less than YOLOv8n and 0.1480 less than YOLOv8x).  
In addition to the evaluation under normal lighting conditions, we also tested the performance of the adversarial textures in low-light (night) conditions, as shown in Figure 7. This figure presents the same evaluation as in Figure 6, but with images taken at night. As expected, the average [EMAIL_ADDRESS] scores were higher under these conditions, likely due to the absence of night images in the training dataset. The results also show that the [EMAIL_ADDRESS] scores of different textures do not follow the same trend observed in Figure 6. Specifically, some textures, such as those optimized on YOLOv8 models, were more robust to low-light conditions than those optimized on YOLOv5 models. Nonetheless, the texture optimized using the combination of YOLOv8n, YOLOv5m, and YOLOv3 continued to perform the best, achieving the lowest average [EMAIL_ADDRESS] (0.2636), even in low-light scenarios.  
Figure 5: Textures and the rendered image of the vehicle optimized by multi-model training.