esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Platelet Aggregation, ATP Release Reaction, and Lactate Dehydrogenase Assay  
After the confirmation of all procedures in this study by directives of the Declaration of Helsinki, the Institutional Review Board of Taipei Medical University (TMU-JIRB-N201812024) was further approved. All human volunteers who participated in this study signed a consent form. Human platelet suspensions were prepared as previously described [15]. Blood was collected from 35 healthy human volunteers (age: 20-35 years) who had taken no medication during the 2 weeks preceding collection. The blood was mixed with an acid-citrate-destrose (9:1, \(v/v\)) solution. After centrifuging for 10 min at 120\(\times\)_g_, the resultant supernatant (PRP) from the centrifugal samples, was supplemented with EDTA (2 mM) and heparin (6.4 U/mL) for 5 min and, again, subject to centrifugation at 500\(\times\)_g_ for 10 min. A 5 mL Tyrode's solution was used to resuspend the obtained platelet pellet and kept for 10 min at 37 \({}^{\circ}\)C. After the spin at 500\(\times\)_g_ for 10 min, the washing procedure was repeated. The washed platelets were suspended in Tyrode's solution that contained BSA (3.5 mg/mL). A Coulter counter (Beckman Coulter, Miami, FL, USA) was applied to count platelet numbers. The final Ca2+ concentration was 1 mM in the Tyrode's solution.  
According to the method described by [PERSON] et al. [15], the platelet aggregation was measured using Lumi-Aggregometer (Payton Associates, Scarborough, ON, Canada). Before adding various agonists, namely collagen, AA, U46619, and thrombin, to the platelet suspensions (3.6 \(\times\) 10\({}^{8}\) cells/mL), they were preincubated with various concentrations of metformin (1.2-1.8 mM) or an isovolumetric solvent control (PBS) for 3 min. The reaction was allowed to proceed for 6 min, and the extent of aggregation was expressed in light transmission units. To measure the release of ATP, 20 \(\upmu\)L of a luciferin-luciferase mixture was added 1 min before agonists were added, and the amount of ATP released in the experimental group was compared with that of the control by using an F-7000 spectrometer (Hitachi, Tokyo, Japan) according to the manufacturer's instructions. Moreover, washed platelets were preincubated with different concentrations of metformin (2-10 mM) or PBS for 20 min at 37 \({}^{\circ}\)C. An aliquot of the supernatant (10 \(\upmu\)L) was deposited on a Fuji Dri-Chem slide (LDH-PIII; Fuji, Tokyo, Japan), and absorbance was read at a wavelength of 540 nm. The highest lactate dehydrogenase (LDH) release was observed in the positive control triton (0.5%)-treated platelets.
# Experimental Study on the Dynamic Response of an Immersed Tunnel Element Moored inside the Harbor
## 3 Results
### Dynamic Responses of a Single Element
#### 3.2.2 Under Wave Conditions  
The maximum tension force of each cable under wave action is shown in Figure 10. For different loading conditions, the cable tension force of the standard element reaches its maximum under a half load condition, in which the maximum tension of the nylon cable reaches 297 kN and that of the steel cable reaches 429 kN. The order of the cable force under different load conditions is: half load \(>\) no load \(>\) heavy load \(>\) full load.  
The typical time-domain motion curves of the immersed element under wave action are shown in Figure 11. The element motion curves all present obvious characteristics of low frequency (corresponding to the natural period of the mooring system) and are superimposed with high frequency (corresponding to the wave period). For the sway and surge components, the low frequency is dominant; however, for all other motion components, the high frequency is dominant. Additionally, the high frequency of roll is the most obvious among these components.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Motion** \\ **Component** \\ \end{tabular} } & \multicolumn{2}{c}{**No Load**} & \multicolumn{2}{c}{**Half Load**} & \multicolumn{2}{c}{**Heavy Load**} & \multicolumn{2}{c}{**Full Load**} \\ \cline{2-9}  & **Offshore** & **Inshore** & **Offshore** & **Inshore** & **Offshore** & **Inshore** & **Offshore** & **Inshore** \\  & **Wind** & **Wind** & **Wind** & **Wind** & **Wind** & **Wind** & **Wind** & **Wind** & **Wind** \\ \hline Surge (m) & - & - & - & - & - & - & - & - & - \\ Sway (m) & 3.42 & 1.35 & 2.14 & 0.87 & 1.55 & 0.45 & 1.87 & 0.71 \\ Heave (m) & - & - & - & - & - & - & - & - \\ Yaw (\({}^{\circ}\)) & 0.19 & 0.30 & 0.16 & 0.19 & 0.18 & 0.10 & 0.17 & 0.13 \\ Pitch (\({}^{\circ}\)) & - & - & - & - & - & - & - & - \\ Roll (\({}^{\circ}\)) & 0.40 & 0.47 & 0.31 & 0.31 & 0.11 & 0.12 & 0.23 & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Motion extremums of a standard immersed element under wind action (center of mass).  
Figure 10: Maximum tension force of mooring lines with a single immersed element moored under wave action.  
The motion extremums of the moored, immersed element are listed in Table 7. Under each load condition, the maximum values of sway under wave action are smaller than those under wind action, while the maximum values of roll are inverse.
# Smart Beamforming for Direct LEO Satellite Access of Future IoT
## 5 Numerical Results
### Number of Terminals  
First we evaluate the estimator that is used to determine the number of sources. The accuracy of the method presented in Section 4 is measured as the mean absolute percentage error (MAPE), which is given by:  
\[\text{MAPE}=\frac{1}{P}\sum_{i=1}^{P}\left|\frac{N_{\text{U}_{i}}-\hat{N}_{ \text{U}_{i}}}{N_{\text{U}_{i}}}\right|. \tag{39}\]  
In Figure 7, we represent the MAPE against the number of users. As it could be easily anticipated, the higher the number of symbols \(N_{\text{S}}\), the lower the mismatch error between the sample-covariance and the real covariance matrices. Then, (29) and (34) become more accurate and thus, the MAPE is reduced. To achieve a given accuracy, it is worth highlighting that array antennas consisting of \(N_{\text{R}}=1024\) elements need less samples than those antennas using \(N_{\text{R}}=576\) elements. Another interesting conclusion that could be inferred from Figure 7 is that the number of errors committed increases with \(N_{\text{U}}\). In most of the cases that the test fails to determine the number of terminals, it follows that \(\hat{N}_{\text{U}}<N_{\text{U}}\). This implies that the error is due to underestimation and thus, some users are missed.
## 4 Validation of the Numerical Model
### Previous Experimental Campaign by [PERSON] et al. [38]
#### 4.1.5 Results  
In order to validate the finite element model used to determine the temperature field distribution, a fraction of the experimental campaign [38] is first replicated--consisting of wood pallets, wood pallets mixing with charcoal, eucalyptus, and pine--all data tested can be seen in [38]. Figure 3 provides an overall view of the experimental setup that was specially designed and built at the Structures and Strength of Materials Laboratory of the Instituto Superior Tecnico--University of Lisbon in Portugal. The temperature measured in the top plate from the experimental campaign was from the firebrands (\(T_{\mathit{Firebrands}}\)) and was from the steel plate in the numerical models, so these values cannot be compared between them, therefore the goal in this validation was to compare the temperature distribution for the middle plate.  
The dashed lines represent the experimental values, and the solid lines, which are the same colours as the experimental ones, represent the numerical values. The first gap is the gap between the bottom side (BS) of the top plate and the top side (TS) of the middle plate. Due to the gap between the steel plates, it is not expected that both the experimental and numerical values are directly comparable, for this reason, to directly compare the experimental values of the thermocouples, an average numerical value of the first gap is used. This means, that it is expected that the temperature field distribution may present some discontinuities between steel plates (Figure 11). Figure 12 shows the numerical results with the experimental values for comparison purposes for the first gap, it is worth noting that the experimental data from the wood pallet had a drop in 800-2000 s due to a malfunction, but it is still possible to compare them with the numerical model. The temperature values were measured at the closest integration Gauss point to the fire contact surface, which is point A in Figure 9.  
Figure 11: Temperature field distribution for wood pallets at 5000 s in Kelvin.  
Figure 13 presents the experimental average of the four types of wood used, with the numerical average of the same woods. This approach is used because there are relevant differences between temperatures that depends on the type of wood used, as can be seen in Figure 12, especially for Wood Pallet-Charcoal and Pine.  
The observation of these results prompts the following remarks: (i) in any case for the first gap, the numerical temperature value provides a good comparison with the experimental ones, but there are significant differences between different types of woods; (ii) using an average for all numerical and experimental values of the temperature over time shows a good fit, then it can be stated that the proposed heat flux for firebrand accumulation model can simulate the temperature field distribution in plane surfaces; (iii) the average approach is acceptable because in a wildfire there are lots of woods types to be burn, thus it is close to the real life situation.
# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 4 Types of Wireless Sensor Network
### Mobile WSNs  
Mobile WSNs is the most recent type where mobile sensor nodes always change their positions and restructure the networks. Mobile WSNs are also called hybrid networks due to their combination of fixed and mobile nodes. When sensor nodes are deployed, information on the field of interest is gathered [49; 67]. To increase the mobile WSNs' mobility, the number of applications is scattered by people, animals, named vehicles, auto vehicles, and unmanned vehicles. Mobile WSNs' applications are widely classified into inventory tracking, on-demand, mining, environmental and seismic monitoring, acoustic and fire detection, healthcare, and meteorology [68]. The main issues and limitations of the mobile WSNs are the mobility of sink/sensor nodes, static and dynamic deployment, hardware cost, energy depletion, protocols and topology design, heterogeneity network, localization, and data redundancy. The routing methods of mobile WSNs are divided into hybrid, distributed, and centralized, while two types of routing approaches are used, which are classical-based and optimized-based. In mobile WSNs, the mostly used existing protocols for communication are angle-based dynamic source routing (ADSR), dynamic source routing (DSR), and low-energy adaptive clustering hierarchy (LEACH) [69]. In a mobile WSN (MWSN), the easier option is to use stationary sensor nodes and move the sink nodes. A query-driven data model used for crops on a farm may contain sensors that detect humidity or temperature, and anytime a farmer passes by, its smartphone functions as a sink node, while allowing the data to be downloaded. When sensor nodes are connected to animals in tracking applications, event data are detected by the static sinks while sensor nodes are mobile. When the animals are within its range, a static sink is utilized to gather tracking data stored in the sensor nodes [70]. On the other hand, target coverage is critical since it is prevalent in applications. In these scenarios, in data gathered, wherever only the data on certain points need to be gathered where the event occurs. Since sensors are generally distributed in a messy manner throughout a large area, moving a subset of mobile sensors is frequently necessary to guarantee adequate coverage of the sensing field [71]. Unmanned aerial vehicles (UAVs) are widely used for data collecting and picture capture in current history. UAVs are used with wireless sensor networks (WSNs) to develop data-collecting systems with a wide range of capabilities. These methods are designed as UAV-WSNs, but they are ideally adapted for remote monitoring and emergency response, particularly in the region of landslides, wildfires, floods, and other disasters applications. Event-driven data model observation is the foundation for making the appropriate judgement in these circumstances. Additionally, event-driven data detection still saves the lives and cost to a certain measure [72].
## 4 Conclusions  
In conclusion, we demonstrated bound-state soliton and NLP generation in a figure-of-eight Tm-doped all-fiber laser setup with dispersion compensation. The developed oscillator generated stretched pulses, NLPs, and bounded solitons at a fundamental repetition frequency of 15.795 MHz. Under a pump power of 513 mW, three bound solitons with duration of 1.4 ps and fixed pulse separations of 12.4 ps were observed. For higher pump power levels, the developed oscillator tended to generate NLPs. At the maximum pump power of \(\sim\)1.6 W, mode-locked NLPs with pulse energy of 2.91 nJ, FWHM spike duration of 509 fs, and 3 dB spectral bandwidth of 18.5 nm were reported. The NLP output spectrum could be tuned by 35 nm, from 1943.5 to 1978 nm, only by adjusting the PC position. To our knowledge, this is the first report of the generation of a tunable NLP in a mode-locked TDFL based on a nonlinear loop mirror saturable absorber. Due to the compact, simple, and all-fiber configuration, the developed oscillator can be a promising source for many applications, e.g., optical communication and material processing.  
Not applicable.  
Data Availability Statement: Data is available on request.  
The authors thank AM Technologies Sp. z o.o. for providing access to their RF spectrum analyzer.  
The authors declare no conflict of interest.
# Simulation Analysis of Nanosecond Laser Processing of Titanium Alloy Based on Helical Trepanning
## 3 Results and Discussion
### Simulation Results
#### 3.2.3 Fluid Velocity Analysis  
To study the movement process of the metal vapor further, the fluid velocity of the metal vapor was analyzed at different times, as shown in Figure 4, where the color indicates the velocity value in m/s. The results of the temperature simulation showed that the metal material evaporates under the action of the laser to generate metal vapor, which is sprayed from the bottom of the small hole to the outside of the small hole under the pressure difference and diffuses into the surrounding air. The velocity field simulation results show that the metal wall of the hole shaft is much higher in the steam velocity or other areas. This may be related to the Gaussian distribution of laser energy. Because the laser energy in the center of the laser beam is high, the central part of the material is easy to gasify. The metal vapor in this part is dense, and the generated vapor pressure is also the largest. Therefore, the metal vapor diffusion rate in the center of the spot is the largest and,  
Figure 3: Micro hole variations with processing time: (**a**) width; (**b**) depth.  
the material removal rate is the highest. In actual laser processing, the metal vapor velocity might be larger than the simulation result due to the expansion effect of plasma.
# State-of-the-Art Review of Structural Vibration Control: Overview and Research Gaps
## 1 Introduction
### Research Significance  
Numerous surveys have explored vibration control strategies for civil structures, with a predominant focus on distinct control system categories. Most of these review papers emphasize the limitations of control systems, impeding their anticipated implementation. Unfortunately, none comprehensively address the experimental challenges of implementing semi-active control for structures. This study presents a state-of-the-art review paper to address this research gap, offering a broader perspective on research efforts to overcome practical implementation challenges in semi-active control systems.  
The contributions of this review include studying and summarizing the various types of semi-active control devices and control algorithms in civil structures, identifying key challenges and limitations in the application of control systems, and reviewing emerging trends in semi-active control strategies to address these challenges and ensure robust performance. This paper provides a realistic evaluation of semi-active control systems, discussing practical issues such as delays, uncertainties, algorithm selection, and system integration. It emphasizes methods to mitigate these undesirable effects and highlights the advantages of semi-active systems in seismic applications. Additionally, it highlights the increasing importance of multi-objective adaptive control algorithms, which address multiple challenges, such as optimizing control parameters, mitigating uncertainties, managing sensor and actuator configurations, facilitating decentralized control, and minimizing time delays.
# A Review: Sample Preparation and Chromatographic Technologies for Detection of Aflatoxins in Foods
## 3 Chromatography
### Liquid Chromatography and Mass Spectrometry  
Since the 1980s, LC-MS has become the fastest growing technique available for mycotoxin analysis. The potential benefits of LC-MS-based mycotoxin analysis have long been recognized and exploited. The technique can achieve simultaneous multimycotoxin determination based on LC separation and mass to charge (_m/z_) of target analytes, an intrinsic property that provides more specific identification based on molecular weight of the target analyte than ultraviolet and visible light absorption or fluorescence emission, extrinsic properties used by LC-UV/FLD. In the 1980s, the limitations that hindered the application of LC-MS were interfaces between LC and MS detectors, limited ionization efficiency of MS detectors and the poor and unstable response of many mycotoxins due to the incomplete desolvation. The advent of commercial LC-MS instruments equipped with electrospray ionization has brought the identification and quantitation of contaminants, mycotoxins and pesticides in foods to another level [239; 240]. The impact of modern LC-MS has been signified by the unmatchable sensitivity in quantitation, specificity in identification and number of mycotoxins that could be analyzed in one analysis. Currently, these highly desirable features make LC-MS the gold-standard for mycotoxin analysis [241].
# Interference Management in Femtocells by the Adaptive Network Sensing Power Control Technique
## 3 System Model  
We considered an inadequately deployed femtocell in a heterogeneous network, with overlays integrated with the eNB. The two systems, i.e., the eNB as the primary system and the femtocell as the secondary system, work in a frequency reuse fashion and have no coordination among them, so neither system pays attention to their respective mutual presence. The only information that the eNB shares with the HeNB is the scheduled information of the macro-cell by using backhaul link. In such an operating scenario, the downlink transmission of the HeNB causes inter-cell interference to the cell-edge macro user. To take into account worst-case interference, we considered that the eNB and HeNb use the same carrier frequency, f = 2 GHz and the same bandwidth. The access mode used in the HeNB has an efficient effect on the whole interference scenario, as shown in Figure 1. Only authorized UE has access to the femtocells deployed in the close access mode; therefore, the MUE in the vicinity of the femtocell has no access to subscribe to the services [21]. We take into account path loss using the path loss model for urban deployments in the 3 GPP LTE-A [22].
# Collaborative Facilitation and Collaborative Inhibition in Virtual Environments
## 3 Results
### Social Interactions Analysis  
To examine the relationship between participants' involvement in the chat line and performance in the DRM task, we analyzed the social interactions that took place in the chat line among members of the collaborative condition groups through [PERSON]'s \(r\) correlations between the subjects' performance and all LIWC categories [50]. Results are shown in Table 5.  
Increased participation in the chat line by group members (LIWC category--Word Count) and addressing a chat message to all members of the group (LIWC categories--2 nd Pers. Plur.; Other people), rather than just one member (LIWC category--2 nd Pers. Sing.) seem to lead to a higher number of studied words within the virtual collaborative groups. However, the using of second-person plural is also related to a higher number of non-studied words. Lastly, communication marked by certainty and the utilization of negations seems to elicit fewer non-studied words. On the contrary, communication characterized by swear words and a distinct consensus within the group seems to be correlated with a smaller number of average studied words.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Group** & **Variable** & \(\mathbf{t}\)**Value** & **Sig.** \\ \hline Males & Studied words Tot Sample & \(-1.971\) & \(p<0.05\) \\ vs. & Individual condition & - & ns \\ Females & Studied words Nominal condition & \(-2.104\) & \(p<0.05\) \\  & Collaborative condition & - & ns \\ \hline Homogeneous male groups & Studied words Tot Sample & \(-2.496\) & \(p<0.01\) \\ vs. & Studied words Individual condition & \(-2.053\) & \(p<0.05\) \\ Homogeneous female groups & Studied words Nominal condition & \(-2.164\) & \(p<0.05\) \\  & Non-studied words Collaborative condition & \(1.983\) & \(p<0.05\) \\ \hline Prevalent male groups & Studied words Tot Sample & \(-2.759\) & \(p<0.01\) \\ vs. & Studied words Individual condition & \(-2.634\) & \(p<0.01\) \\ Prevalent female groups & Studied words Nominal condition & \(-2.413\) & \(p<0.05\) \\  & Collaborative condition & - & ns \\ \hline Homogeneous groups & Total responses Tot Sample & \(2.096\) & \(p<0.05\) \\ vs. & Individual condition & - & ns \\ Heterogeneous groups & Non-studied words Nominal condition & \(2.008\) & \(p<0.05\) \\  & Studied words Collaborative condition & \(2.946\) & \(p<0.001\) \\  & Total responses Collaborative condition & \(2.389\) & \(p<0.05\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Gender effect. The table shows the characteristics of the group composition in terms of gender and their effect on the subjects’ performance. A positive t value means that the group mentioned first in the table has, compared to the second group, a significantly higher average and vice versa.
###### Abstract  
Postharvest diseases cause considerable losses of fruits and vegetables during transportation and storage, and synthetic fungicides are the first option for their prevention. However, promising alternatives to chemical fungicides are currently available, and several post-harvest diseases can be controlled using microbial antagonists. This study utilised a comprehensive methodology to assess the antagonistic and synergistic interactions between four yeasts and two bacteria in conjunction with sodium bicarbonate (SB) during the treatment of sweet cherries and plums. The aim of this study was to evaluate the effects of microbial antagonists and sodium bicarbonate on fruits. The in situ treatments showed a protective effect exerted by the antagonists _P. Guillermondii_ and _H. uvarum_, and their combination with SB. However, in vivo studies did not indicate enhanced efficacy when combined with this compound. In conclusion, the use of microbial antagonists in conjunction with SB has been successful in preventing post-harvest rot of cherries and plums. Tests conducted on live organisms have shown that microbial antagonists are viable alternatives to synthetic fungicides for the control of stone fruit rot.  
biological control; hyperparasitism; microbial antagonists; post-harvest rot; stone fruit