esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Antibacterial Activity against MDR Bacterial Strain
#### 3.3.1 Antibacterial Activity against MDR _S. aureus_ Strains  
Except SA1 that was susceptible to oxacillin (a narrow-spectrum antibiotic closely-related to methicillin), the majority of _S. aureus_ strains evaluated in this study were MRSA, i.e., methicillin-resistant _S. aureus_, responsible for infections associated with higher healthcare costs and mortality rate, due to resistance to beta-lactam antibiotics [8]. The search for effective compounds against MRSA remains a great challenge. The clinical isolate SA6 was the one with the highest resistance in this panel, with proven resistance to six antibiotics belonging to various classes such as penciillins, aminoglycosides, fluoroquinolones, and fusidanes (Table A1). The Table 3 reports the Rf values of all inhibition bands visible on bioautograms against the different MDR _S. aureus_ strains. Generally, fewer bands were observed compared to the reference strain of _Staphylococcus aureus_, and MeOH extracts affected less the growth of MDR strains than EtOAc extracts, which is consistent with the results from the preliminary screening on wild-type bacteria. Methanolic extracts from Agarales species did not have any effect on the growth of these MDR strains. Conversely, both low-polarity and polar extracts from _Fomitopsis pinicola_ were found to contain several antibacterial compounds affecting all bacterial strains. Interestingly, some differences of selectivity could be observed. As seen in Figure 3, bioautograms are slightly different according to the _S. aureus_ strain. While some compounds inhibited only the wild strain (Rf between 0.20-0.30, zone **e**), others inhibited all strains, such as the compound **a** at Rf 0.75. More surprisingly, some inhibition bands were observed only against MDR strains, like the band **b** present in the four bioautograms with clinical isolates, or the bands **c** and **d**inhibiting SA2, SA6, and SA8. An inhibition band around Rf 0.15 was found to be specific to SA2, and was not even present in the bioautogram of the reference strain of _S. aureus_ (**f**). Therefore, these results indicate that several compounds from the widespread brown-rot mushroom _F. pinicola_ can prevent the development of MRSA strains. Regarding _Scleroderma citrium_, the four samples collected in different times and places displayed variable contents of antibacterial compounds in EtOAc extracts. This could be  
Figure 3: TLC bioautograms obtained with the EtOAc extract from _Fomitopsis pinicola_ against the wild type strain of _S. aureus_ (SA) ATCC 29,213 and the four MDR strains SA1, SA2, SA6, and SA8. The green color observed for SA6 could be due to a poor reduction of MTT. Compounds **a–f** are discussed more in details in the text regarding their selectivity.
## 4 Validation of Inelastic Seismic Analysis
### Analysis Modeling  
#### 4.1.1 Dynamic Characteristics  
The used finite element model for the test mockup is shown in Figure 3. To confirm the dynamic characteristics of the analysis model, resonance searching tests were carried out, with the random input motions having the frequency range of 1.0 to 100.0 Hz. Figure 13 presents the frequency response functions measured by Accelerometer-3 (shown in Figure 5).  
As shown in Figure 13, the measured dominant resonance frequencies are about 11.75 Hz for the 1 st mode and 28 Hz for the 2 nd mode. Table 8 presents the comparison of the resonance frequencies between tests and analyses.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Directions**} & \multirow{2}{*}{**PGA**} & \multicolumn{2}{c}{**Target Input**} & \multicolumn{2}{c}{**Shaking Table**} & \multicolumn{2}{c}{**End of Pipe**} \\  & & \multicolumn{2}{c}{**Motions**} & \multicolumn{2}{c}{**(Accelerometer-1)**} & \multicolumn{2}{c}{**(Accelerometer-3)**} \\ \cline{3-8}  & & **ZPA (g)** & **ZPA Ratio \({}^{*}\)** & **ZPA (g)** & **ZPA Ratio** & **ZPA (g)** & **ZPA Ratio** \\ \hline EW (X) & 0.3 g & 1.18 & 2.0 & 1.71 & 1.95 & 5.55 & 1.57 \\  & 0.6 g & 2.35 & 2.0 & 3.33 & 8.76 & 1.57 \\ \hline NS (Z) & 0.3 g & 0.86 & 2.0 & 1.47 & 2.12 & 9.26 & 1.37 \\  & 0.6 g & 1.75 & 2.0 & 3.13 & 2.12 & 12.70 & 1.37 \\ \hline V (Y) & 0.3 g & 1.33 & 1.86 & 1.86 & 6.23 & 1.95 & 6.23 & 1.58 \\  & 0.6 g & 2.65 & 2.0 & 3.62 & 1.95 & 9.88 & 1.58 \\ \hline \end{tabular} \({}^{*}\) The ZPA ratio represents the ratio of ZPA responses corresponding to PGA = 0.6 g and PGA = 0.3 g.  
\end{table}
Table 7: Test results of ZPA responses.  
Figure 12: Test results of acceleration responses at Accelerometer-3 for PGA = 0.3 g: (a) EW (X); (b) NS (Z); (c) V (Y).  
As shown in Table 8, the dominant modes for EW and NS directions are almost the same in the tests but slightly different in the modal analysis results. In the test results, the frequency of Mode 1 is the same in both directions because it is not easy to separate such a closed mode in the test. However, it is confirmed that the used seismic analysis model describes the dynamic characteristics of the test mockup in good agreement.
## 2 Methodology
### Assumptions  
Our research on the tracking system showed us that, in reality, they are from the same capacity compared to a static configuration and will produce more energy at any point in time, but at what cost? When talking about the price, we have to do a test for 10 years of operation. Thus, we must add the cost of the land into the calculations without forgetting to consider the electricity price, which increases every year, and the state degradation of the PV panels, which degrade every year. Therefore, we must consider the average output per kW installed and the maintenance price for the tracking system every year. Finally, we must calculate the cost per watt supplied and installed for the static system and tracker system. Moreover, that question can be asked in reverse. How much does the by-watt price have to drop before the static system becomes more viable than the tracker system?  
When comparing to ascertain what is the best system, static versus tracking, many assumptions need to be made in addition to the reality of actual energy production and how it matches with site loads, etc. Our scientific research confirms that many assumptions are common between the static system and the tracker system, but some are not. Based on the assumptions made, we can say that the tracker system is more economically feasible.  
Compared to fixed panels, the advantage of mobile panels is that the sun moves continuously during the day, while a PV panel is fixed in its position, thus losing a considerable amount of energy that might be available. When a photovoltaic panel is fixed in relation to the ground and oriented towards the south, its energy yield is not constant during the day. At the start and end of the day, the poor radiation angle of the panel reduces the efficiency of electricity production [11; 12; 13; 14; 15; 16; 19].
## Materials and Methods
### Canopy FPAR Measurements  
The canola canopy FPAR measurements were performed after the UAV image acquisition using the LP-80 PAR/LAI Ceptometer (AccuPAR, Decagon Devices Inc., Pullman, WA, USA). The LP-80 probe contained 80 independent sensors, which were evenly spaced 1 cm apart. The photosensors measured the PAR in the 400 to 700 nm waveband (\(\mathrm{\mu mol/(m^{2}s)}\)). During the FPAR measurement, the probe was placed along the row direction within the canopy. Four positions and directions were considered to obtain comprehensive PAR data, i.e., the incident PAR, the top of canopy reflected PAR, the bottom of canopy transmission PAR and the bottom of soil reflection PAR. Each plot was repeated five times to calculate the average value and the canola canopy FPAR was then calculated using Equation (1) [31]. A summary of the statistics of the canola FPAR acquired in situ is given in Table 1.  
\[\mathrm{FPAR}=\frac{\left(I_{\mathrm{c}\downarrow}-I_{\mathrm{c}\uparrow}- \left(I_{\mathrm{uc\downarrow}}-I_{\mathrm{uc\uparrow}}\right)\right)}{I_{ \mathrm{c}\downarrow}} \tag{1}\]  
where \(I_{\mathrm{c}\downarrow}\) is the solar downward radiation at the canopy top, \(I_{\mathrm{c}\uparrow}\) is the solar radiation emitted from the canopy, \(I_{\mathrm{uc\downarrow}}\) is the solar downward radiation under the canopy and \(I_{\mathrm{uc\uparrow}}\) is the solar radiation reflected by the soil under the canopy.
## 1 Introduction
On this basis, variational Bayesian inference is carried out to achieve adaptive noise covariance estimation. The method is mainly used to recurse the joint posterior distribution of the target state and covariance matrix through decomposition of the fixed form distribution of the factors. In comparison, the framework of variational Bayesian approximation can not only effectively reduce the operation cost, but also achieve the purpose of adaptive joint estimation. We hope to explore an innovative filter that can perform stably under the condition of unknown covariance of measurement noise. By introducing the variational Bayesian approximation into the framework of the TPHD filter (VB-TPHD), we model a matrix of covariance as a random matrix of inverse Gamma distribution and estimate the noise covariance sequence adaptively with the passage of tracking time by combining the joint distribution of the inverse gamma and Gaussian. The trajectories can also be determined more precisely. VB-TPHD can avoid labeling each Gaussian component and managing the track after filtering [35]. In the specific implementation part of the algorithm, we propose the AGM-VB-TPHD filter to be applied to the linear Gaussian model under the condition of unknown newborn intensity [9]. For the nonlinear Gaussian model [36; 37], we also provide two solutions, namely the AEK-VB-TPHD filter and AUK-VB-TPHD filter. Two simulation results show their feasibility. Under the condition of unknown and time-varying covariance of measurement noise, the VB-TPHD filter performs better trajectory tracking than the tag-VB-PHD filter does [20; 34], and it also has stronger robustness. The rest of the paper is organized as follows: Section 2 introduces the background and material on trajectory PHD filters. In Section 3, we give the derivation of equations and theoretical basis of the VB-TPHD filter. In Section 4, the implementation of the VB-TPHDfilter under linear and nonlinear conditions is given in detail. Section 5 simulates the tracking performance of the VB-TPHD filter. Finally, Section 6 draws a conclusion.
## 3 Results
### Fundamental Characteristics of CYTOP NP Deposition
#### 3.1.1 Influence of the CYTOP-Glass Slide Distance on the NP Deposition  
To elucidate the influence of the CYTOP-glass slide distance on the properties of deposited CYTOP NPs, a glass slide was obliquely placed on the CYTOP substrate, as shown in Figure 2. In this experiment, a fs laser beam with a pulse energy of 5.0 J/pulse before the objective lens was scanned at the CYTOP surface with the speed of 1500 \(\upmu\)m/s. After the laser scanning, the NP-deposited areas on the glass surface 100 to 10,000 \(\upmu\)m from the CYTOP substrate were observed by SEM to characterize the NP deposition tendency at different CYTOP-glass slide distances.  
Figure 3 shows SEM images of CYTOP NPs deposited onto the glass slide at different CYTOP-glass distances and the relationship between the diameter of the NPs and the CYTOP-glass slide distance. The average diameter of the NPs was calculated by counting NPs in \(200\times 200\) nm\({}^{2}\) areas of the glass slide at each distance. The calculation was repeated five times. When the CYTOP-glass slide distance was less than 6000 \(\upmu\)m, NPs approximately 10-100 nm in diameter agglomerated with other NPs. When the distance exceeded 6000 \(\upmu\)m, particles 10-30 nm in diameter were uniformly distributed on the glass slide. Figure 3 shows that the particle size gradually decreases with increasing CYTOP-glass distance.  
Figure 1: Schematic of the holographic femtosecond (fs) laser processing setup for CYTOP nanoparticle (NP) deposition. (OL: objective lens, L: lens, PBS: polarization beam splitter).  
It seems that two different types of surface morphology composed of NP aggregates or relatively small individual NPs can be created on the glass slide depending on the CYTOP-glass slide distance.
## 4 Results and Discussion  
Figure 6 shows the experimental set, which was constructed to control the amount of rotor eccentricity. To measure the back EMF at the rated speed, a back-to-back system was constructed, wherein one servo motor and one test motor were arranged back to back. In addition, the rotor eccentricity was precisely controlled using two gap sensors. Table 1 lists the specifications of the motor. Figures 7 and 8 show the input current from the inverter to the PMSM based on the eccentricity. Figure 7a-c indicates ec = 0, 1, and 1.5 mm, respectively. Even when eccentricity occurred, the current magnitude was almost the same in each case; however, it was observed that additional harmonics were generated in each case. To compare the harmonics more precisely, the FFT results of each case were analyzed, except the zeroth to third harmonic components. In the experiment, the initial position was determined using an encoder, however it is considered not smooth due to the inverter switching frequency. Figure 9 shows the core loss when the fundamental current wave was applied, and Figure 10 shows the core loss when harmonics were considered in the applied current. The current used in the core loss analysis was derived from the experimental results. It was confirmed that when harmonics were considered in the applied current, the ripple and average value increased. Comparing the average value of each case, the values increased by approximately 12%, 11.5%, and 10.6%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(f\) (Hz) & \(K_{h}\) (w/m3) & \(K_{e}\) (w/m3) & \(K_{a}\) (w/m3) & \(n_{st}\) \\ \hline
50 & 199.9 & 0.67 & 0 & 2 \\ \hline
60 & 195 & 0.77 & 0 & 2 \\ \hline
100 & 181.1 & 0.12 & 7.8 & 2 \\ \hline
200 & 182.4 & 0.5 & 4.4 & 2 \\ \hline
400 & 199.9 & 0.67 & 0 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Core loss coefficient based on curve fitting.  
Figure 8: Experimental fast Fourier transform (FFT) results of input current: (a) ec = 0 mm, (b) ec = 1 mm, and (c) ec = 1.5 mm.  
Figure 6: Experimental setup.  
Figure 7: Experimental results of input current: (a) ec = 0 mm, (b) ec = 1 mm, and (c) ec = 1.5 mm.  
Figure 9: Core loss when fundamental current was applied: (a) ec = 0 mm, (b) ec = 1 mm, and (c) ec = 1.5 mm.
###### Abstract  
The significance of long-span bridges being susceptible to wind-induced vibrations and the need for evaluating their aerodynamic performance is the focus of this study. The main emphasis is on experimental methods for assessing the bridges' aerodynamic stability, using sectional model tests with the free vibration technique. The dynamic properties of the model are determined from the measured response, using various system identification methods, including the modified Ibrahim time domain (MITD) and iterative least squares (ILS) for two-degree-of-freedom systems and the logarithmic decrement method (LDM) and the Hilbert transform method (HTM) for single-degree-of-freedom (SDOF) systems. A new dynamic testing setup was designed to facilitate single-degree-of-freedom (heave and pitch) and coupled two-degree-of-freedom (2 DOF) motion in a wind tunnel section model. The vertical and torsional stiffnesses of the model were adjusted with elastic springs. A Great Belt Bridge section model was selected for testing due to its streamlined aerodynamic shape. The direct and crossflow derivatives were extracted from the measured response using the system identification methods mentioned. Additionally, analytical studies and numerical computational fluid dynamics simulations were conducted to validate the experimental results. The study found that HTM is most effective in SDOF due to its ability to extract both damping and frequency from the nonlinear response, whereas the MITD method is faster in converging system parameters in 2 DOF system tests. The experimental and numerical results are comparable to the flat plate, which confirms the streamlined behavior of the Great Belt section from an aerodynamic perspective.  
long-span bridges; flutter derivatives; system identification methods; wind tunnel test; free vibration test; computational fluid dynamics
# DPS Clustering: New Results
## 2 Materials and Methods
### DPS-Algorithms
#### 2.2.2 SDPS-Algorithm  
Historically, the first in a series of DPS-algorithms was the set-theoretic SDPS. It is based on the density \(S\), which has the name \"Number of points\" (\"Amount of space\") [2,3,6] and conveys the degree of concentration of space \(X\) around each of its points \(x\) (the most natural understanding of the density \(X\) in \(x\)).  
The density \(S_{A}(x)\) depends on the localization radius r and the non-negative parameter \(p\), which takes into account the distance to \(x\) in the ball \(D_{A}(x,r)\):  
\[S_{A}(x)=S_{A}(x|p)=\sum_{y\in D_{A}(x,r)}\bigg{(}1-\frac{d(x,y)}{r}\bigg{)}^{ p}. \tag{9}\]  
With \(p=0\), we obtain the usual number of points, which explains the name \(S\):  
\[S_{A}(x|0)=|D_{A}(x,r)|.\]  
The density \(S\) is \(r\)-local, and the SDPS-algorithm is the operation of a DPS-scheme based on \(S\): \(\text{SDPS}=DPS(S,\beta,r)\).  
The result of SDPS-condensing in \(X\Leftrightarrow\) sets locally containing \"many \(X\)\". They are formally the most consistent with empirical clusters. By varying the SDPS parameters, one can obtain a fairly complete idea of the hierarchy of clusters in \(X\).  
The SDPS-algorithm will serve as a testing ground where new results for the DPS-series algorithms will be tested and shown.
# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
###### Abstract  
In recent years, virtual learning environments are gaining more and more momentum, considering both the technologies deployed in their support and the sheer number of terminals directly or indirectly interacting with them. This essentially means that every day, more and more smart devices play an active role in this exemplary Web of Things scenario. This digital revolution, affecting education, appears clearly intertwined with the earliest forecasts of the Internet of Things, envisioning around 50 billions heterogeneous devices and gadgets to be active by 2020, considering also the deployment of the fog computing paradigm, which moves part of the computational power to the edge of the network. Moreover, these interconnected objects are expected to produce more and more significant streams of data, themselves generated at unprecedented rates, sometimes to be analyzed almost in real time. Concerning educational environments, this translates to a new type of big data stream, which can be labeled as educational big data streams. Here, pieces of information coming from different sources (such as communications between students and instructors, as well as students' tests, etc.) require accurate analysis and mining techniques in order to retrieve fruitful and well-timed insights from them. This article presents an overview of the current state of the art of virtual learning environments and their limitations; then, it explains the main ideas behind the paradigms of big data streams and of fog computing, in order to introduce an e-learning architecture integrating both of them. Such an action aims to enhance the ability of virtual learning environments to be closer to the needs of all the actors in an educational scenario, as demonstrated by a preliminary implementation of the envisioned architecture. We believe that the proposed big stream and fog-based educational framework may pave the way towards a better understanding of students' educational behaviors and foster new research directions in the field.  
big data; big data streams; fog computing; e-learning; virtual learning environment; Internet of Things +
Footnote †: journal: _Future Internet_
## 2 Network Model
### The Time Model  
Nodes in DUANs are always floating and mobile as time varies. Therefore, we should define a time model to indicate the mobility pattern. The time model designed in this paper comprises 3 different kinds.  
* Sampling period \(t_{0}\): sampling period \(t_{0}\) is the basic time unit during the localization process, nodes in the networks will communicate with each other during each \(t_{0}\); \[t_{0}=N\times\frac{1}{f}\] where \(f\) is the frequency of high frequency mobility pattern described in the last section, \(N\) is a proper sampling ratio.
* Estimation period \(T_{0}\): Estimation period \(T_{0}\) is comprised by some \(t_{0}\) which is used for the mobility pattern estimation. \[T_{0}=N_{0}t_{0}\]
* Test period \(T_{s}\): \(T_{s}\) is used for re-estimation procedure to eliminate the error accumulation.  
The time model is as shown in Figure 3.
## 4 Analysis of Influencing Factors
### Influence of the Supporting Pile Wall Size
#### 4.3.2 Tunnel Vertical Displacement  
Figure 13(b) depicts the vertical displacement curve of the tunnel lining's outer wall and the tunnel's maximum vertical displacement upon completion of the foundation pit excavation, varying with different sizes of supporting pile walls. With an increase in the thickness of the equivalent ground wall from 600 mm to 1200 mm, the uplift on the north side of the tunnel decreases by 2.17%, 3.26%, 4.35%, 8.70%, 9.78% and 14.13%, respectively, while the settlement of the south side of the tunnel decreases by 5.66%, 10.19%, 14.34%, 19.62%, 25.66% and 29.81%, respectively. The tunnel's vertical displacement diminishes progressively with increasing thickness of the equivalent ground wall (i.e., the size of the supporting pile wall), indicating that the alteration in the wall thickness (i.e., the size of the supporting pile wall) significantly influences tunnel settlement. Taking the tunnel deformation control standard in the existing specification as a reference, the vertical displacement of the tunnel under each equivalent wall thickness (i.e., the size of the supporting pile wall) significantly exceeds the early warning threshold.
## 4 Experimental Results and Comparative Analyses
### Performance Evaluations
#### 4.2.2 Comparison of Contextual Embedding Techniques  
Our experimental study focused on evaluating the performance of different contextual embedding models in a DTM in 6G. We investigated four models: the Baseline (SBERT model: all-MiniLM-L6-v2), PatentSBerta [33], SciBert [63], and Roberta [64]. The comparison was conducted across five parameters: NSTs, RISs, ISAC, ISAGSC, and EAIs. Afteranalyzing the data presented in the two tables (Tables 3 and 4), it is clear that no single model can be declared an absolute winner across all the evaluated parameters.  
The baseline model shows a relatively high NST score of 0.4900, yet it does not consistently outperform other metrics. In some cases, PatentSBerta has slightly lower NSTs but higher ISAC than the baseline. SciBert demonstrates strong performance in RISs with a value of 0.5090, but its performance in other metrics is not uniformly superior. Roberta also has a mixed bag of results, with some metrics showing comparable values to the others.  
This lack of a winner indicates that the choice of model depends on the specific metric of interest. However, if overall balanced performance is desired, a more in-depth analysis of each metric's weights and importance would be necessary. Moreover, these results suggest that there is room for further improvement in developing these models to achieve more consistent and superior performance across all the evaluated metrics.