esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Heavy Metal Pollution Assessment  
#### 3.2.1 Contamination Factor (CF)  
The spatial distribution characteristics of _CF_ for HMs in sedimentary deposits are presented in Figure 3. The degree of pollution of As, Zn, Cd, and Pb in the sediments was higher than that of Cr, Ni, and Cu. Cr, Ni, and Cu were basically unpolluted, while Zn and Cd had similar pollution levels, with lower pollution in the upstream and downstream and higher pollution in the lower reaches. The pollution level of As in the sediments was relatively high, with most areas being highly polluted. In addition, the pollution status of Pb in the sediments was mainly low, with some areas experiencing moderate pollution.  
Figure 2: Characteristics of HM concentrations in YRB sediments. (**a**) Cr. (**b**) Ni. (**c**) Cu. (**d**) Zn. (**e**) As. (**f**) Cd. (**g**) Pb.  
Based on the measured concentrations of seven heavy metals in the sediments at 44 sampling points, we used the contamination factor (_CF_) method to evaluate the degree of heavy metal pollution (Figure 4). Most HMs in the sediments are in the \"low to moderate\" pollution range. Zinc (Zn) has a higher pollution level in the upstream area, while arsenic (As) has a higher pollution level in the downstream area. In the upstream area, the average order of _CF_ results is the following: As (6.61), Zn (2.37), Cd (1.15), Pb (0.86), Ni (0.59), Cr (0.58), Cu (0.43). In the upstream, As was found at severe pollution levels in 21.05% of the sampling points and at high pollution levels in 78.95% of the sampling points. Zn was detected at high pollution levels in 15.79% of the sampling points and at moderate pollution levels in 84.21% of the sampling points.  
Figure 3: Spatial characteristics of the contamination factor. (**a**) Cr. (**b**) Ni. (**c**) Cu. (**d**) Zn. (**e**) As. (**f**) Cd. (**g**) Pb.  
In the midstream area, As was found at severe pollution levels in 50% of the sampling points and at high pollution levels in the remaining 50%. Additionally, Zn was found at moderate pollution levels in 57.14% of the sampling points. In the downstream area, the average order of _CF_ values is the following: As (9.69), Cd (1.22), Pb (1.17), Cr (0.50), Ni (0.47), Zn (0.38), Cu (0.37). All sampling points in the downstream area are severely polluted with As, and 18.18% of the sampling points of Cd are in the high pollution state.
# Detection of Smoking in Indoor Environment Using Machine Learning
## Materials and Methods
### Material
#### 2.1.2 Indoor Smoking Environment-Testing Set  
In order to imitate real life indoor air quality, a small room (16 m\({}^{2}\)) was conducted and applied as a testing set of supervised learning. We target the changes of TVOC, Humidity (H), Temperature (T),  
\begin{table}
\begin{tabular}{c c c c} \hline
**Type of Cigarette** & **Puff (Count)** & **Tarr (mg)** & **Nicotine (mg)** \\ \hline cigarette 1 & 5, 7, 10 & 5 & 0.35 \\ cigarette 2 & 3, 5, 7 & 3 & 0.2 \\ cigarette 3 & 5, 7, 10 & 5 & 0.4 \\ cigarette 4 & 3, 5, 7 & 1 & 0.1 \\ \hline \end{tabular}
\end{table}
Table 1: Information of used cigarette.  
Figure 1: Rotary smoking machines (**a**) RM200A2 of BORGWALDT (**b**) Operation of Smoking Machine RM200A2 (in Seoul, Korea).  
Figure 2: The experimental atmosphere: temperature and humidity changes.  
CO\({}_{2}\) and PM 2.5, PM 10, emitted to the surrounding atmosphere before/after smoking of conventional tobacco. We ventilated through the window before/after the smoking to initialize the indoor air conditions. All the tests are conducted in the initial air condition of TVOC, H, T, CO\({}_{2}\), PM 2.5 and PM 10 as Table 2. During the experiment, T, H are controlled separately by the air conditioner. Additionally, the experiment shows that smoking is not related with smoking but opening/closing the window well affects the temperature and humidity of the room.  
The used cigarettes are not defined, since the experiment was occurred by volunteer smokers. The room conditions are only controlled on the material we are focusing, as Table 2, while other factors are not considered. As the considered factors are limited, this testing set is considered as reflection of real situation. Heating and cooking are also performed in the same room with same condition in non-smoking situation to create comparing situation. The heating source is an electric heater which consume 400 W, without humidity changes. For cooking environment, Portable butane gas stove was applied with 600 g of meat (port, beef) on the pan.
# A Reduced-Order Model for the Vibration Analysis of Mistuned Blade-Disc-Shaft Assembly
## 3 Reduced-Order Modeling
### Parametric Projection of Blade Mistuning  
The mistuning projection method proposed by [PERSON] et al. [30] is employed in this section to include blade mistuning in the reduced-order model. The core idea of this method is to project the mistuning matrix onto the normal modes of a tuned blade cantilevered at its root. The main procedures of this method are briefly reviewed herein. Firstly, the blade part mode \(\mathbf{\Phi}_{t}^{b}\) is represented by cyclically symmetric modes as shown in Equation (7).  
\[\mathbf{\Phi}_{t}^{b}=\left(\mathbf{E}\otimes\mathbf{I}_{N_{b}}\right) \underset{h=1,2,\cdots,N}{\mathrm{Bdiag}}\left(\mathbf{\widetilde{\Phi}}_{h}^ {b}\right), \tag{13}\]  
where \(\mathbf{\widetilde{\Phi}}_{h}^{b}\) denotes the blade part of \(\mathbf{\widetilde{\Phi}}_{h}^{s}\), and \(\mathbf{I}_{N_{b}}\) is an identity matrix of dimensions \(N_{b}\), where \(N_{b}\) denotes the number of DOFs in a sector model of blade.  
Then, the blade part's cyclically symmetric modes are represented by the normal modes of the tuned blade cantilevered at its root.  
\[\mathbf{\widetilde{\Phi}}_{h}^{b}=\mathbf{\Phi}_{fix}^{b}\mathbf{\widetilde{ p}}_{h}^{b}, \tag{14}\]  
where \(\mathbf{\Phi}_{fix}^{b}\) is the normal mode matrix of the tuned cantilevered blade, and \(\mathbf{\widetilde{p}}_{h}^{b}\) denotes the participation factor. By the above transformation, \(\mathbf{\Phi}_{t}^{b}=\left(\mathbf{I}_{N}\otimes\mathbf{\Phi}_{fix}^{b} \right)\mathbf{\widetilde{p}}^{b}\), where \(\mathbf{\widetilde{p}}^{b}=\left(\mathbf{E}\otimes\mathbf{I}_{N_{b}}\right) \underset{h=1,2,\cdots,N}{\mathrm{Bdiag}}\left(\mathbf{\widetilde{p}}_{h}^{b }\right)\). As the blades are not directly coupled, \(\mathbf{K}_{\delta}^{b}\) has a block-diagonal form as \(\mathbf{K}_{\delta}^{b}=\underset{h=1,2,\cdots,N}{\mathrm{Bdiag}}\left( \mathbf{K}_{\delta,h}^{b}\right)\). Then, the blade part's reduced-order mistuning matrices \(\mathbf{\widetilde{K}}_{\delta}^{b}\) can be expressed as  
\[\mathbf{\widetilde{K}}_{\delta}^{b}=\left(\mathbf{\widetilde{p}}^{b}\right)^{ \mathrm{T}}\underset{h=1,2,\cdots,N}{\mathrm{Bdiag}}\left(\mathbf{\widetilde{K }}_{\delta,h}^{b}\right)\mathbf{\widetilde{p}}^{b}, \tag{15}\]where \(\overline{\mathbf{K}}_{5,h}^{b}=\left(\mathbf{\Phi}_{fix}^{b}\right)^{\text{T}} \mathbf{K}_{0,h}^{b}\mathbf{\Phi}_{fix}^{b}\). As \(\overline{\mathbf{K}}_{5,h}^{b}\) tends not to be diagonal, it is often approximately processed in frequency mistuning models by keeping only the diagonal elements, which can be represented as  
\[\overline{\mathbf{K}}_{5,h}^{b}\approx\underset{m=1,2,\cdots,M}{\text{diag}} \left(\delta_{h,m}^{b}\Lambda_{m}^{b}\right), \tag{16}\]  
where \(M\) is the number of retained modes of the tuned cantilevered blade, \(\Lambda_{m}^{b}\) represents the \(m\)th eigenvalue of the tuned cantilevered blade, and \(\delta_{h,m}^{b}\) denotes the deviation ratio, which is often set to be a random parameter during analysis.
# QoSComm: A Data Flow Allocation Strategy among SDN-Based Data Centers for IoT Big Data Analytics
## 5 Performance Evaluation
### Phase One: Network Performance  
#### 5.1.1 Iperf  
We used IPERF to test network performance between hosts H1 to H6, H2 to H5, and H3 to H4, applying Controller default and QoSComm data flow forwarding methods in the simulation model. IPERF was configured with default values and set time interval to 30 s for each data transfer. The IPERF server was running in H6, H5, and H4 hosts and the client in H1, H2, and H3 hosts. We repeated the experiment 20 times for UDP and TCP data flows.  
Figures 5 and 6 show IPERF UDP and TCP average results in two sets: individually (i) and simultaneously (s) tests. As it is observed in Figure 5, only in H1 to H6 individual tests was Controller data transfer rate higher than QoSComm, and this is because Controller had a higher bandwidth capacity path (100 mbps) compared to QoSComm selected path (90 mbps). For network stress testing running host-to-host simultaneously transfers, QoSComm obtained a higher data transfer rate compared to Controller improving application data transfer performance.  
Figure 5: IPERF UDP data transfer comparison (average mbps). Tests execution: (i) individually; (s) simultaneously. Time interval: 30 s. STDEV: H1-H6i (1.40, 0.81), H2-H5i (0.69, 2.23), H3-H4i (0.97, 1.79); H1-H6s (2.15, 4.02), H2-H5s (1.40, 2.12), H3-H4s (1.21, 3.46).
# A Two-Stage Co-Evolution Multi-Objective Evolutionary Algorithm for UAV Trajectory Planning
## 3 General Framework of MOUTP Based on TSCEA
### Multi-Objective Model  
#### 3.1.1 Establishment of the Objective Function  
In this section, two optimization objective functions of MOUTP are considered: the UAV flight distance and the sum of the UAV's distance to the obstacle threat. From the perspective of actual UAV flight, the UAV and obstacle flight safety distance constraints and the UAV's maximum flight height, flight slope, and flight angle constraints were established. This study constructed the MOUTP model using two optimization objective functions and five constraints.  
The first objective function was the sum of the UAV's flight distances, \(f_{1}\):  
\[f_{1}=\sum_{k=0}^{P_{N}}\|K_{k+1}-K_{k}\| \tag{4}\]  
where \(P_{N}\) denotes the total number of key trajectory points in the UAV's trajectory, \(K_{0}\) denotes the position of the UAV, \(K_{P_{N}+1}\) denotes the position of the mission point, \([K_{1},K_{2},\ldots,K_{P_{N}}]\) denotes the position of each key trajectory point, and \(\|\cdot\|\) denotes the calculation of the Euclidean distance between two points.  
The second objective function is the sum of the UAV's distance from the terrain and the simulated radar obstacle threat, \(f_{2}\). In order to avoid collisions between the UAV and obstacles, the generated key trajectory points should be far away from the terrain and obstacle points:  
\[f_{2}=\sum_{r=1}^{R}\frac{\lambda}{\|P_{k}-P_{r}\|}+\sum_{k=1}^{P_{N}}Terrain _{k} \tag{5}\]  
where \(R\) represents the number of radars, \(P_{k}\) represents the coordinates of the UAV at key trajectory point \(k\), \(P_{r}\) represents the position of the center of radar \(r\), \(\lambda\) represents the threat  
Figure 3: Execution framework of UAV trajectory planning based on TSCEA.  
coefficient of the radar, and \(Terrain_{k}\) represents the threat coefficient of the terrain and the key trajectory point \(k\).
# A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research
## 2 Neurotransmitter Detection Methods
### Optical Sensing Techniques  
#### 2.2.1 Surface-Enhanced Raman Spectroscopy (SERS)  
SERS uses photon interactions with matter to detect and identify molecules. This Raman spectroscopy technique enhances the Raman scattering by molecules adsorbed on an irregular metal surface, with an amplification factor that can reach 10 to 100 billion [100]. The method requires a functionalized surface for each molecule, and it can be made sensitive enough to detect single molecules [101]. The signal is enhanced by two main effects: electromagnetic and chemical enhancements. The enhancing materials are usually made of noble metals, e.g., Au/Ag nanorods and Au/Ag nanoparticles, and they are used as substrates to generate high electromagnetic fields. However, the SERS signals derived from individual nanoparticles are often too weak for detection; for high SERS signals, nanoparticle assemblies with tunable gaps can improve the electromagnetic fields. In general, the smaller the gap, the stronger the signal [102].  
Suitable SERS substrates were designed to detect various neurotransmitters by adding silver colloids [103], silver nanoparticles [104] or hollow core photonic crystal fiber [105] to the cell culture medium. SERS is used to detect concentrations of choline (Limit Of Detection (LOD): 2 uM), acetylcholine (LOD: 4 uM), dopamine (LOD: 100 nM) and epinephrine (LOD: 700 uM) [104], glutamate (LOD: 100 nM) and GABA (LOD: 100 uM) [105]. In the case of dopamine, an LOD of 6 fM was achieved using Au@Ag nanorod dimers based on aptamer that are thiolated, which DNA specifically binds to dopamine [102].  
Although noble metallic nanorods and nanoparticles coupled with aptamers permit a highly sensitive and selective SERS, some of them are toxic or cause irreversible aggregations. Thus, techniques such as the one described in [102; 104; 105] are usually not usable for in vivo diagnosis.
# LCAS-DetNet: A Ship Target Detection Network for Synthetic Aperture Radar Images
## 3 Materials and Experiments
### Experimental Setting  
In this study, our algorithm was implemented using the PyTorch 1.8.1 framework and used the NVIDIA Quadro RTX 5000 GPU for accelerated computing. We employed SGD as the optimizer, with an initial learning rate of 0.01 and momentum of 0.937. The batch size was four, and the training epochs were fixed at 200.
## 5 Experimental Results  
To test our time-varying filtering structure with a real-life signal, we measured the output of a magnetic encoder mounted on a geared DC motor. Figure 10 presents the measurement setup to acquire the test signal.  
The obtained measurement is presented in Figure 11, along with the responses of the time-invariant and time-varying filters.  
Figure 12 presents the closeup of the most interesting part of the responses.  
Figure 10: Measurement station with a geared DC motor and a Tektronix MSO 4034 oscilloscope.  
As one can notice, the time-varying filter had a much better performance, in terms of settling time, than the time-invariant counterpart. Furthermore, the time-varying filter was characterised by a considerably reduced overshoot.
# AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain
## 1 Introduction
It is an essential part of intrusion detection systems (IDS) and aids in the identification and detection of harmful network activity. This revolution in agricultural technology is built on having access to all the data produced by every sensor used on a farm, centralized via the internet, and analyzed for the best possible decision-making [11]. Any sensor employed generates a significant amount of data that can be extensively examined and provides useful information to enhance crop quality or lessen environmental impact. Cloud computing, physical hardware, and the IoT are the three key components that enable the automation and data sharing needed by this new smart agriculture [12]. With the help of technical improvements (drones, sensors, software, etc. ), we can complete agricultural jobs faster and more effectively than humans. Agriculture 4.0 must make sure that an appropriate security mechanism is implemented to avert an attack to build scalable and secure systems. The integrity of data is important for the effective functioning of information technological advances such as data analysis and intelligent devices [13]. Because agriculture 4.0 incorporates elements from conventional Internet, cellular, and wireless technologies, which may include all of the security risks that advancements present situation. It also addresses current security risks such as data and device integrity, consistency, and accessibility. Devices in agriculture 4.0 are implemented in outdoor spaces where they are subjected to external factors such as ani mals, human beings, or farming equipment. Unknowingly, these factors remove or damage sensors. Since the 6 th century B.C., there has been a threat known as Agro terrorism [5]. This type of terrorism could have a variety of goals, including causing financial harm, fear, and social unrest [6; 7]. Terrorists could incite community disturbance and lack of credibility in administration by exploiting agricultural and food corporate crises, which can lead to undivided attention in the world. Cyber-agro-terrorism in agriculture 4.0 is an electronic system in agricultural settings to harm crops and animals while causing economic loss. Cyber-agro-terrorists can operate attacks on fields and through the internet, using cyber resources [8].
## 3 Results and Discussion
### Cytotoxicity Tests  
The results of the cytotoxicity tests are shown in Figure 5.  
No statistically significant differences were found between the cell viability of human keratinocyte HaCaT cells cultivated in the extract of the CNFs0.1% composite films with respect to that of the calcium alginate or control culture medium.  
These results show the first antiviral study of calcium alginate against a non-enveloped double-stranded DNA virus. The negative electric charge of this type of biopolymer is believed to be the main mechanism involved in its antiviral activity [31; 32]. Thus, alginate's antiviral activity may be caused by blocking the decapsulation process of the non-enveloped virus protein on the surface of the cell membrane [33]. Here, we report also the first study of the antiviral activity of calcium alginate/CNFs composites against this type of non-enveloped virus. CNFs possess also a negatively charged surface and have a high surface/volume ratio [27; 34]. Therefore, the enhancement of the antiviral activity of calcium alginate could be attributed to the increase of negative charge even with the addition of a low amount of CNFs. These results are in good accordance with those obtained with other carbon-based nanomaterials (CBNs) tested against other non-enveloped viruses such as norovirus [35], novel duck reovirus [36], and bacteriophage MS2 [37]. Nonetheless, further work is needed to study the antiviral activity of these calcium alginate/carbon nanofibers composite materials against a potentially pathogenic naked virus to validate the results obtained here for potential antiviral biomedical applications. CBNs are very promising broad-spectrum antimicrobial biomaterials because they are characterized by a low risk of microbial resistance [29; 38; 39]. Furthermore, alginate-based materials have  
Figure 5: Cell viability (%) of extracts of calcium alginate (at 100% \(v/v\)) and calcium alginate/CNFs films (at 100% and 10% \(v/v\)), culture medium (negative control), and 1000 \(\upmu\)M zinc chloride cytotoxic solution (positive control) in human keratinocyte HaCaT cells. Only significant statistical differences were found with the negative control. \(\mathbf{***}\)\(p\) > 0.001; ns: not significant.  
shown antiviral capacity against enveloped positive-sense single-stranded RNA viruses similar to SARS-CoV-2: human immunodeficiency virus type 1, rubella virus, hepatitis C virus, and sindbis virus [19; 40; 41]. Therefore, these calcium alginate/carbon nanofibers composites are very promising for many biomedical applications in need of non-toxic materials with antiviral and antibacterial capacity for the current and future pandemics.
## 3 Results and Discussion
### Electromagnetic Parameters and Loss Mechanisms of Composites
With the continuous increase in CB content, the interfacial polarization is continuously enhanced, and the peak value rises continuously. In addition, the small peak at low frequency mainly corresponds to dipole polarization, which can be attributed to the surface defects of CB and the existence of polar functional groups [25]. The dielectric loss can be explained by the Debye medium theory [26; 27]. The Cole-Cole curve, which is the relationship of \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\), can be obtained as shown in Equation (2) [28; 29; 30]:  
\[\left(\varepsilon^{\prime}-\frac{\varepsilon_{s}+\varepsilon_{\infty}}{2} \right)^{2}+\left(\varepsilon^{\prime\prime}\right)^{2}=\left(\frac{ \varepsilon_{s}-\varepsilon_{\infty}}{2}\right)^{2} \tag{2}\]  
where \(\varepsilon_{s}\) and \(\varepsilon_{\infty}\) are the static permittivity and relative dielectric permittivity at infinite frequency limit, respectively. Each semicircle in the Cole-Cole curve represents a polarization relaxation process, and the appearance of a tail at the end of the curve indicates conductance loss [31; 32]. As shown in Figure 8, all composites exhibited a few semicircles, suggesting that there may be multiple dielectric relaxation losses: interfacial polarization and dipole polarization, which is in accord with the relaxation peaks (as described in Figure 6c). Compared with 0% CB/PLA, in 5% CB/PLA, the tail was more apparent with a higher slope, indicating that conduction loss increased with CB content. At the same time, the number and diameter of the semicircles decreased, which may be ascribed to strong conductivity and hidden polarization loss [20; 33]. The above results show that dielectric loss is the primary loss mechanism of CB/PLA composites. When the CB content is low (0%, 5%), polarization relaxation is the main loss mechanism; when the CB content is high (10%, 15%, 20%, 25%), there is a synergistic effect of polarization relaxation loss and conduction loss. Figure 7: Conductivity of different CB/PLA composites.
## 6 Conclusions  
The non-Gaussian property and its influence on extreme wind pressures on the combined long-span cylindrical roof are systemically investigated in this study. The sensitive regions that exhibit strong non-Gaussian properties are found. The main findings are as follows:  
1. Wind tunnel test results indicate that wind pressures on the combined cylindrical roof are left-skewed softening processes when the worst case among all tested wind azimuths is considered. Regions around curved edges along the span of the cylindrical roof are revealed as having strong non-Gaussian properties.
2. The HPM is verified by wind tunnel test results being capable of calculating peak factors and extreme values of non-Gaussian wind pressures on the combined cylindrical roof with a relative error of no more than 5%.
3. All the maximal peak factors are larger than 5.0, and 20% of them are even larger than 7.0. Using peak factors following the Gaussian assumption or 2.5 underestimates the extreme values of wind pressures by 40-50%.
4. It is necessary to include the non-Gaussian properties when estimating the extreme values of wind pressure on such long-span cylindrical roofs in engineering practice.  
It is noted that the results presented in this paper are preliminary based on the wind tunnel test results for the scaled model of the roof. Therefore, further work is needed to create a full-scale model of the L-shaped long-span cylindrical roof and investigate it numerically.  
Conceptualization, S.W. and C.Z.; methodology, S.W.; software, S.W.; validation, S.W., C.Z. and Q.S.; formal analysis, S.W.; investigation, S.W.; resources, S.W.; data curation, S.W.; writing--original draft preparation, S.W.; writing--review and editing, C.Z. and Q.S.; visualization, S.W.; supervision, C.Z. and Q.S.; project administration, Q.S.; funding acquisition, Q.S. 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 grant number 51978570.  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
This research was financially supported by the National Natural Science Foundation of China (Grant No. 51978570). The authors gratefully acknowledge the support.  
The authors declare no conflict of interest.