esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effect of HR2302-30E and C3G on RBL-2H3 Cell Viability  
An MTT assay was performed to evaluate the effect of HR2302-30E on the viability of RBL-2H3 cells. As shown in Figure 3, the viability of RBL-2H3 mast cells treated with various concentrations of HR2302-30E and C3G was evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) assay. Notably, both HR2302-30E (500, 1000, and 2000 ug/mL) and C3G (5 and 10 ug/mL) did not significantly affect the cell viability compared to the negative control; thus, the extract is not cytotoxic to the selected cell line.  
Figure 3: Effects of HR2302-30E and C3G on the viability of RBL-2H3 mast cells. Cell viability was measured using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) assay. Negative control: untreated cells. Data are represented as the mean \(\pm\) SEM of three independent experiments. The statistical analyses were performed using the Dunnett \(t\)-test. PP2 is a general Src family kinase inhibitor. \(-\), No treatment.  
Figure 2: High-performance liquid chromatography (HPLC) chromatograms and photodiode array (PDA) spectra of cyanidin-3-glucoside (C3G) (**a,c**) and HR2302-30E (**b,d**).
# Improved PR Control Strategy for an LCL Three-Phase Grid-Connected Inverter Based on Active Damping
## 2 Mathematical Model and Control Method
### Instantaneous Power Calculation  
The calculation equation of instantaneous active power in the three-phase static coordinate system is as follows:  
\[p=u_{\mathrm{a}}i_{\mathrm{a}}+u_{\mathrm{b}}i_{\mathrm{b}}+u_{\mathrm{c}}i_{ \mathrm{c}} \tag{4}\]  
Equivalent transformation is carried out on Equation (4):  
\[p=\left[u_{\mathrm{a}}\;u_{\mathrm{b}}\;u_{\mathrm{c}}\right]\left[\begin{array} []{c}i_{a}\\ i_{b}\\ i_{c}\end{array}\right]=\left[\begin{array}{c}\left(C^{-1}\right)^{T}\left[ \begin{array}{c}u_{a}\\ u_{b}\\ i_{c}\end{array}\right]\end{array}\right]^{T}\cdot C\left[\begin{array}{c}i_{ a}\\ i_{b}\\ i_{c}\end{array}\right]=\left[\begin{array}{c}\left(C^{-1}\right)^{T}C^{-1}C \left[\begin{array}{c}u_{a}\\ u_{b}\\ u_{c}\end{array}\right]\end{array}\right]^{T}\cdot C\left[\begin{array}{c}i_{ a}\\ i_{b}\\ i_{c}\end{array}\right] \tag{5}\]  
From the [PERSON] transformation equation, it can be seen that:  
\[\left[\begin{array}{c}u_{\alpha}\\ u_{\beta}\\ u_{0}\end{array}\right]=C\left[\begin{array}{c}u_{\mathrm{a}}\\ u_{b}\\ u_{c}\end{array}\right],\left[\begin{array}{c}i_{\alpha}\\ i_{\beta}\\ i_{0}\end{array}\right]=C\left[\begin{array}{c}i_{\mathrm{a}}\\ i_{b}\\ i_{c}\end{array}\right] \tag{6}\]  
where the transformation matrix is:  
\[C=\frac{2}{3}\left[\begin{array}{ccc}1&-\frac{1}{2}&-\frac{1}{2}\\ 0&\frac  
With the above instantaneous power expression, the grid-connected inverter can be controlled by the power outer loop.
Appl. Sci._**2015**, \(5\), 666-681; doi:10.3390/app5040666  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
A Near-Hover Adaptive Attitude Control Strategy of a Ducted Fan Micro Aerial Vehicle with Actuator Dynamics  
**[PERSON] * and [PERSON]**  
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics,  
29 YuDao Street, Nanjing 210016, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-25-8489-2305; Fax: +86-25-8489-2301.  
Academic Editor: [PERSON]  
_Received: 11 August 2015 / Accepted: 23 September 2015 / Published: 28 September 2015_  
**Abstract:** The aerodynamic parameters of ducted fan micro aerial vehicles (MAVs) are difficult and expensive to precisely measure and are, therefore, not available in most cases. Furthermore, the actuator dynamics with risks of potentially destabilizing the overall system are important but often neglected consideration factors in the control system design of ducted fan MAVs. This paper presents a near-hover adaptive attitude control strategy of a prototype ducted fan MAV with actuator dynamics and without any prior information about the behavior of the MAV. The proposed strategy consists of an online parameter estimation algorithm and an adaptive gain scheduling algorithm, with the former accommodating parametric uncertainties, and the latter approximately eliminating the coupling among axes and guaranteeing the control quality of the MAV. The effectiveness of the proposed strategy is verified numerically and experimentally.  
**Keywords:** parameter estimation; adaptive gain scheduling; attitude control; ducted fan micro aerial vehicle
## 3 Results
### Full Network Adjustment of 5560 Unified Control Points (2008-2021)
It reveals a balanced distribution of control points across both urban and rural areas, ensuring that the adjustment results are geodetically representative of the national territory. The high density of UCPs in the central and southern regions reflects installation priorities in earlier project phases, while peripheral areas such as coastal zones and Jeju Island are also well covered. Figure 10: Spatial distribution of 5560 unified control points (UCPs) used in the nationwide network adjustment from 2008 to 2021. This integrated adjustment forms the foundation of a stable and homogeneous national geodetic framework. By applying uniform modeling procedures and a consistent set of reference stations across the entire network, the methodology ensures high positional integrity, comparability of coordinates across time, and alignment with global geodetic standards. It also enhances traceability and enables future reprocessing as part of long-term geodetic infrastructure management.
# Thermal Mapping of Power Semiconductors in H-Bridge Circuit +
## 4 Thermal Stresses of Power Devices at Various Loading Conditions
### Dominating Factors for Device Loading
In the case of various fundamental frequencies, the loss dissipation and thermal stress of the power devices are shown in Figure 7. It can be seen that, regardless of the different fundamental frequencies, the loss breakdown and the mean junction temperature of the power semiconductors stay the same. However, the smaller fundamental frequency causes the higher junction temperature swing due to its sensitivity to the thermal constant of the power module. Figure 5: Impacts of various power factors (PFs) on power module. (**a**) Loss dissipation. (**b**) Mean junction temperature. (**c**) Junction temperature swing. Figure 6: Impact of various loading currents (I\({}_{\text{lo}}\)) on power module. (**a**) Loss dissipation. (**b**) Mean junction temperature. (**c**) Junction temperature swing. Figure 7: Impact of various fundamental frequencies (f\({}_{1}\)) on power module. (**a**) Loss dissipation. (**b**) Mean junction temperature. (**c**) Junction temperature swing. The dependence on the switching frequency is depicted in Figure 8. It is noted that the switching loss is in line with the switching frequency, in which the switching frequency at 15 kHz has the highest switching loss in the IGBT and in the diode.
# Low Profile Monopole Meander Line Antenna for WLAN Applications
## 3 Working Principles
### Current Distribution  
Analysis and discussion of the simulated current distribution are conducted in order to gain insight into the properties and operating principles of the proposed antenna design. Figure 3 depicts the duplicated distribution of surface current for the proposed antenna at 2.4 GHz in order to save space and to be more concise. As can be seen from Figure 3, the current of the proposed printed ML monopole antenna is concentrated at the lower edge of the upper meander line and it can also be observed that the current concentrates on both sides of the antenna port, as illustrated in the Figure, which explains why the meander line increases the electrical line of the current path.
## 4 Conclusions  
This paper underlines that parameters determined from non-destructive and electrochemical testing can be used as an alternative or even as an improvement to the traditional parameters of load, stress, deformation and strain to determine the fatigue life of metallic materials and components. This has been demonstrated for the metastable AISI 347 (1.4550) steel in an initial and aged condition with a clear view on nuclear engineering. Changes in temperature, electric resistance, tangential magnetic field strength or in open circuit potential are among those NDT-based techniques from where measurements can be derived, which show adequate if not even better performance when compared to mechanical stress-strain hysteresis, being one of the state-of-the-art measurement techniques. Parameters derived from those NDT techniques can possibly be easier accessed when compared to plastic strain measurements, and they may possibly be more sensitive when looking at specific cases, where this article is only providing a limited example and can still not claim to demonstrate the full potential of such an approach lying ahead.  
Figure 15: Comparison of predicted crack depth ranges according to Equation (18) for all measured cracks (fatigue tests with a total strain amplitude of 0.3 \(\times\) 10\({}^{-2}\)) is shown in Figure 11.  
It has been shown for media conditions, such as distilled water, as well as reactor pressure vessel boiling water (BWR), that NDT measurements still hold, and that the NDT-based parameters can even be derived appropriately under such environmental conditions. Since the NDT-based parameters derived can be used to determine S-N curves, even in the context of short-time procedures where only a fraction of effort is required to generate an S-N curve when compared to state-of-the-art, a completely new field of generating S-N data is opened.  
Considering components in nuclear engineering often being exposed to strain-controlled loading conditions, at least in the locations where fatigue damage becomes critical, the StrainLife method has been specifically developed and explained based on electrical resistance measurements. It is based on the fact that materials' fatigue data for the stage until crack initiation can be used and transferred to evaluate and assess the fatigue life of components. In that context, the question of what damage--or better, degradation--is, is still not sufficiently solved. However, there is hope for improvement. The current view is still that cracks, and specifically their size, are a characteristic of damage and a structure's vulnerability. Numerical simulation has proven that an understanding between loading conditions and degradation criteria (damage) within a material and a structure can be well established and can help to further improve the reliability of materials' fatigue data generated, specifically with respect to short-time procedures, such as shown with the assessment of crack initiation uncertainties in the case of StrainLife here.
## 3 Resolution of Degeneracy
### Degeneracy Analysis  
During the triangulation process, the camera coordinate systems \(c_{0}\) and \(c_{1}\) intersect the two planes formed by the projection lines on the two frames in a 3D straight line, forming the reconstructed spatial line. The 3D plane can be represented by a camera coordinate system and projected 2D line features:  
\[\begin{array}{c}\pi_{i}=[\begin{array}{ccc}\pi_{ix}&\pi_{iy}&\pi_{iz}&\pi_ {iw}\end{array}],\\ \pi_{ix}c_{ix}+\pi_{iy}c_{iy}+\pi_{ix}c_{iz}+\pi_{iw}=0,\\ \left[\begin{array}{c}\pi_{ix}\\ \pi_{iy}\\ \pi_{iz}\end{array}\right]=[s_{i}]\times e_{i}\end{array} \tag{4}\]  
where \(\pi_{i}\) is the \(i\)th plane vector, \(s_{i}\) and \(e_{i}\) are the endpoints of the tracked line segment, and their cross product can represent the value \((\pi_{ix},\pi_{iy},\pi_{iz})\). From the equation and the camera center \(c_{i}=(c_{ix},c_{iy},c_{iz})\) can calculate \(\pi_{iz}\).  
The direction vector of a line feature projected onto a camera frame can be calculated as follows:  
\[d=\begin{bmatrix}\pi_{0z}\pi_{1y}-\pi_{0y}\pi_{1z}\\ \pi_{0x}\pi_{1z}-\pi_{0z}\pi_{1x}\\ \pi_{0y}\pi_{1x}-\pi_{0x}\pi_{1y}\end{bmatrix} \tag{5}\]  
As the line features approach the poles, the two plane vectors \(\pi_{0}(\pi_{0x},\pi_{0y},\pi_{0z})\) and \(\pi_{1}(\pi_{1x},\pi_{1y},\pi_{1z})\) are parallel to each other so that each element in (5) is 0 and \(d\) becomes a zero matrix. In actual measurements, the estimation of the direction vectors will contain noise, which will lead to errors in the actual calculated values. Therefore, when the degeneracy phenomenon occurs, the direction vectors in Plucker coordinates will be anomalous and the mapping effect of line features will also be affected.  
In the measurement model of line features, the optimized values are represented by transforming the Plucker coordinates into orthogonal coordinates. The reprojection of the line feature to the image plane can be expressed as follows:  
\[l^{\prime}=\begin{bmatrix}l_{1}\\ l_{2}\\ l_{3}\end{bmatrix}=Kn^{c} \tag{6}\]  
where \(K\) represents the camera's intrinsic parameter; \(n^{c}\) represents the normal vector of the reprojected line feature. Since the projection plane in the optimization process is normalized and \(K\) is a unit matrix, the reprojection of a spatial line is equivalent to a normal vector.  
From the above analysis, it can be concluded that the degeneracy phenomenon of line features affects direction vectors in the Plucker coordinates, but the reprojection error at the back end is only related to normal vectors, so the process of solving for optimum value cannot solve the degeneracy problem.
# Conditional Knowledge Extraction Using Contextual Information Enhancement
## 5 Experiment
### Results Analysis
#### 5.2.3 Ablation Experiment  
In our proposed approach, we conducted an ablation experiment to evaluate the effectiveness of combining dependent syntactic analysis with the attention mechanism and semantic information of sentences. Specifically, we evaluated our framework's performance with an attention mechanism and contextual sentence information. Moreover, we evaluated our fine-tuning methods. Results indicate that our proposed method significantly improves the performance of syntactic analysis in both settings, and fine-tuning methods are effective.  
On the cpd-cn dataset, the result of Table 5 compared to Table 4 demonstrates that using sentence dependency grammatical relations to assign weights to the attention mechanism based on WoBERT significantly improves all indicators. It highlights the usefulness of pre-including sentence dependency grammatical relations followed by information matching. However, after we added BiLSTM to \(WoBERT+attention\), the F1-value dropped by 0.95%. It is because WoBERT has difficulty identifying conditional contextual information. However, CBERT initially underperforms WoBERT with only the attention mechanism, but after adding the BiLSTM layer, the model's F1-value improved by 4.97%. It is because  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset Name** & **Models** & **Acc** & **F1** & **Rec** & **P** \\ \hline cpd-cn & \(\begin{matrix}CBERT+attention+BILSTM(\text{ours})\\ \textit{WoBERT}\end{matrix}\) & **86.61\%** & **86.65\%** & **86.14\%** & **87.17\%** \\ \hline  & \(\begin{matrix}CBERT+attention+BILSTM(\text{ours})\\ \textit{WoBERT}\end{matrix}\) & 83.93\% & 83.21\% & 83.17\% & 83.24\% \\ \hline  & \(\begin{matrix}CBERT+attention+BILSTM(\text{ours})\\ \textit{BERT}+attention+BILSTM(\  
the attention mechanism alone does not capture enough contextual information, but the BiLSTM layer enhances the model's ability to learn and leverage context information, demonstrating the effectiveness of the local conditional context enhancement.  
On the cpd-en dataset, Table 6's experimental results show that using BiLSTM to add semantic information leads to an F1-value drop by 7.47%, possibly due to the difficulty for BERT to recognize conditional contextual information. However, CBERT initially performed less well than BERT when using only the attention mechanism. However, with the addition of the BiLSTM layer, the model's F1-value improved by 2.6%. These results emphasize the importance of our local conditional context enhancement approach.  
In addition, we conducted an ablation experiment to assess the effectiveness of model fine-tuning using phrase and entity granularity methods. The experiment compared the performance of the model using only phrase granularity with that using both phrase and entity granularity on the cpd-cn and cpd-en datasets, respectively. The results in Table 7 demonstrate that the model performs better when entity granularity is added, proving the effectiveness of our approach.
## 2 Related Issues: State-of-the-Art
### What Channels Could Be Used for Emotion Detection?  
In the literature, there are some works related to the automatic emotion detection process. Many of them are related to facial expression analysis through artificial vision [11; 12; 13] and to user voice analysis to detect the emotion of the interlocutor [14; 15; 16; 17; 18; 19]. Moreover, just a few describe multimodal systems (using several input channels) for detecting emotions [20; 21; 22], although none of them is implemented in a dialog system for HRI.  
[PERSON] [23] stated that, for humans, some emotions are better identified by voice, such as sadness and fear, but others are better detected through facial expression analysis, such as happiness and anger. Besides, [PERSON] [24] showed that these two modalities give complementary information, since the performance of the system increases when both of them are considered together. This same idea seems to be also corroborated by [PERSON] [25], who obtained a success rate of 60% when using the voice channel and of 57% in the case of using the visual one, but, considering just one multimodal classifier, the success rate increases up to 72%. Moreover, a complete survey of the use of the audio and visual channels for detecting spontaneous emotions is presented in [26].  
[PERSON] [27] complements the facial features extraction with the addition of a non-intrusive channel of information: the image obtained from an infrared camera. In this work, there is a classifier for each of the three channels, and then, the information given by each of them is fused together. Again, they obtained better results when using the whole system in comparison with the results obtained with each channel separately. The success rate is 60% using the audio channel, 56% with the visual one, 48% with the infrared image and 85% when using the whole system.  
On the other hand, the information received by the audio and vision channels could be also used in other ways to detect the user emotion, such as making an emotional analysis of the information transmitted verbally, using natural language processing techniques, as shown in [28; 29; 30], analyzing previous dialogs with the user and analyzing his body pose, too [31; 32].  
Finally, it is worth mentioning that during the last two decades, several studies have been focused on emotion detection using brain-machine interfaces. They are based on the analysis of the neural activity of the users [33; 34]. The most used technique is electroencephalography (EEG) signal analysis. These works explain how to work with these signals and how to extract information using the Discrete Wavelet Transform (DWT)-this is the transform that allows the processing of the non-stationary signals obtained from the EEG. In these cases, the success rate is between 55% and 76%.
## 3 Development of Microsimulation Model
### Validation of Microsimulation Model  
Validation of the model was undertaken by comparing the output simulation results of the model with independent data that were observed during data collection and were not considered in the form of calibration [32]. Validation is an assessment of the quality of calibration and thus ensures the reliability and accuracy of the prediction of the traffic model. The validation of the simulation model was undertaken using the GEH statistic [34] using Equation (3):  
\[GEH=\sqrt{\frac{2*(M-C)^{2}}{M+C}} \tag{3}\]  
where:  
\(M\) = traffic volume from model;  
C = real-world traffic count.  
Regarding traffic modeling of the initial state, when comparing traffic volumes, a GEH coefficient of less than 5 indicates a good validation of the simulation model. Furthermore, 85% of the total traffic volume should have a GEH coefficient of less than 5. Conversely, if the coefficient is greater than 10, the results of the comparison indicate a lack in the models' ability to represent the actual situation [34].  
According to [34], it is necessary to place the generated travel times within a deviation of 15% to 85% of travel routes to compare the accuracy of travel times. These data, which were collected by field testing, must also be correlated with the actual time (peak hours, day of the week) being modeled. The accuracy of the travel time is greater for the validation of public transport vehicles due to their predetermined routes and the smaller amount of data for comparison. Pedestrian flows can significantly affect the movement of traffic. The impact of pedestrian flows is especially pronounced at right turns at signalized intersections and significantly affects the reduction in the level of service.  
The validation of individual flows was assessed using the GEH statistic described earlier. In the morning peak period, the number of traffic intersection approaches with a GEH coefficient of less than 5 was 59/65, equal to 90.7%, whereas in the afternoon peak period, the number of intersections with a coefficient of less than 5 was 57/65, i.e., 87.7% (Table 1).  
Validation of PT in the model was performed by comparing the travel times of terms that run along the entire observed corridor. The travel time must not deviate by more than 15% from the actual measured time. In the morning peak period, deviations for individual lines between the measured and modeled values ranged from 5.89% to 15.38%, whereas in the afternoon peak period, these values ranged from 3.33% to 13.3%. Figure 7 shows the validation of travel time of the longest tram line in study area.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 5 Security Analysis
### _Informal Analysis_
#### 5.2.2 Perfect Forward Secrecy  
If the long-term keys \(s\), \(x_{i}\), and \(y_{j}\) of the cloud server are exposed to an attacker, the attacker can compute the long-term keys of the vehicle and the fog node, \(N_{i}\) and \(N_{j}\). However, the attacker cannot recognize both the long- and short-term keys of the network simultaneously (Section 3.2) because, even if the attacker knows the values of \(N_{i}\) and \(N_{j}\), the attacker cannot determine the value of \(R_{i-j-cs}\), consisting of the random values \(r_{1}\), \(r_{2}\), and \(r_{3}\). Therefore, the attacker cannot calculate the session key, comprising the long-term keys \(N_{i}\) and \(N_{j}\) and short-term key \(R_{i-j-cs}\). Therefore, the proposed protocol can safeguard perfect forward secrecy.
# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 2 Related Work
### SP Approaches  
Recently, the problem of placement in CDCs has been studied extensively. In this section, we present a categorization of previous studies that have proposed architectures and approaches for VM placement. Furthermore, we present analyzed metrics from previous approaches, according to the objective.