esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
### Analysis of Volatile Essential Oils  
Many studies have pointed out that the use of ethanol as a co-solvent can enhance the efficiency and range of SFE, effectively increasing the hydrophilic compound selectivity of citrus peel. Because ethanol contains a hydroxyl group that can form hydrogen bonds with other groups, it attracts the polar molecules of citrus peel when used in SFE [13; 14; 15]. In this study, 10% ethanol was used as a co-solvent in SFE, and the results showed that the respective oleoresin extraction rates for the Ponkan, Tankan, and Murcott citrus varieties were 1.1%, 2%, and 0.8% (\(w/w\)). Citrus oleoresin extracted via SFE is composed of volatile compounds, a fact which can be applied in identifying specific citrus species when utilizing gas chromatography/mass spectrometry (GC/MS) for composition identification. The results of the present study indicated that, across the different samples tested, 33 volatile compounds were identified, 23 of which were present in the Ponkan samples, 27 of which were present in the Tankan samples, and 28 of which were present in the Murcott samples. Divided by functional groups, these compounds consisted of 11 kinds of monoterenes, 7 kinds of sesquiterpenes, 9 kinds of aldehydes, 3 kinds of alcohols, 2 kinds of esters, and 1 kind of ketone (Table 1). The terpene compounds of citrus oleoresin do not contribute to its flavor and fragrance. However, these terpene compounds are the main components of oleoresin, accounting for 95.11% of Ponkan oleoresin, 96.75% of Tankan oleoresin, and 94.92% of Murcott oleoresin. The main monoterepenes in the oleoresin samples from the three citrus varieties were limonene, \(\alpha\)-pinene, and \(\beta\)-myrcene. The reaction mechanisms of all monoterepne synthases start with the ionization of the geranyl diphosphate (GPP) substrate. The resulting carbocation can undergo a range of cyclizations, hydride
## 4 Simulations and Experimental Test
### Experiment  
In this subsection, an experiment is carried out on a tilt tri-rotor UAV designed by our lab to further verify the effectiveness of the proposed control scheme. The proposed cascade controller and control allocator are built in Pixhawk. To ensure flight safety, the aircraft is controlled manually, such that the desired position signal can be provided by the remote device. In the experiment, the tilt tri-rotor UAV first takes off vertically to a certain altitude. Then, the remote device adjusts the desired position signal, and the UAV flies to track the desired position signal. After the flight, it lands at the designated position. The experimental scene is shown in Figure 12.  
The experiment results are shown in Figures 13-15. Figure 13 gives the position variations of the tilt tri-rotor UAV when it tracks the position signal given by remote control. The results show that the actual position curve is consistent with the desired position signal, which means that the proposed controller can guarantee good position tracking effect. In the proposed control scheme, the position control is achieved by the adjustment of the attitude, and the output of the position loop is the desired signal of the attitude loop. The tracking performance of the attitudes is shown in the Figure 14. In the flight process of the hover mode, the actual flight attitude can basically track the reference signal given by the position loop, but there will be a small tracking error. The pulse width modulation (PWM) inputs of the three motors are shown in the Figure 15. The experimental results show that the proposed control scheme in this paper has a good control effect on the position control and attitude control of the tilt tri-rotor UAV in the hover mode, and the application of this scheme will lay the foundation for the further control scheme design of the mode transition mode.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**States** & **SMC-AD** & **SMC-RBFNN** & **PID** \\ \hline \(X\) & 0.905 & 12.820 & 4.388 \\ \(Y\) & 1.227 & 8.260 & 6.885 \\ \(Z\) & 1.051 & 14.710 & 11.70 \\ \(\phi\) & 0.542 & 1.656 & 1.253 \\ \(\  
Figure 14: Attitude trajectories.  
Figure 13: Position trajectories.
Although extensive studies have been carried out to simulate human-structure interactions using a single-degree-of-freedom mass-spring-damping model [47; 48; 49], the research on the dynamics of coupled systems when pedestrians are under walking conditions requires more experimental data to supplement. Nevertheless, the excitation of the human body by a shaking table, which is commonly used in the field of biomechanics, is higher than that of the human body by the real structure. Therefore, the human body parameters obtained by this method are not applicable in the field of civil engineering. The excitation of the human body by means of an excitation shaking table fails to obtain the parameters of the human body in the process of walking. It is thus necessary to carry out test experiments of human-structure interaction on real structures, with the aim to supplement the data on the dynamic parameters when pedestrians interact with the structure and explore the effect of pedestrians on the dynamic properties of the structure as well. In this paper, we introduce the fundamental theory of structural dynamic parameter identification and explore the method of obtaining pedestrian dynamic parameters through a coupled pedestrian-structure system by treating the pedestrian as a single-degree-of-freedom mass-spring-damper system. The relationship between pedestrian body parameters (frequency and damping ratio) and walking frequency and body mass is obtained by identifying the parameters of 25 individual pedestrians walking on the floor slab. The relationship between different statures and the pedestrian dynamics parameters is explored as well. The results are of some guidance for the engineering design of structures that consider pedestrian-structure interactions. The structure of this paper is as follows. In Section 2, the test set-up of a low-frequency steel-concrete composite slab for the identification of pedestrian's dynamic properties of a human-structure interaction system is presented. The identification method and test cases are also introduced in this section. In Section 3, the identification results of the pedestrian's dynamic properties are presented. Conclusions are drawn in Section 4.
# Antennas for Licensed Shared Access in 5G Communications with LTE Mid- and High-Band Coverage
## 3 Dual-Band Antenna for LSA Bands and LTE Mid and High Bands  
In this section, the design and performance of a dual-band antenna (Ant#2) are described.  
Figure 4: Predicted antenna efficiency and gain of the planar LSA antenna (Ant#1).  
Figure 5: Normalized radiation patterns of the planar LSA antenna (Ant#1) at 1.8 and 2.6 GHz.
## 2 Materials and Methods
### Data
SSPs define how societal choices can influence radiative forcing by the end of the century and are based on five narratives describing alternative socioeconomic developments: sustainable development (SSP1), middle-of-the-road development (SSP2), regional rivalry (SSP3), inequality (SSP4), and fossil-fueled development (SSP5) [62]. In this study, two representative coupled SSP-RCP scenarios (SSP2-RCP4.5 and SSP5-RCP8.5) for three periods (the 2030s, 2040s and 2050s) were used. SSP2-RCP4.5 represents a moderate social vulnerability pathway with medium forcing, signifying a middle-of-the-road scenario with moderate challenges in both mitigation and adaptation [63; 64]. In this pathway, social and economic trends remain relatively consistent with historical patterns [59]. Studies by [PERSON] et al. [65] highlighted that the SSP2 marker implementation represents an extension of the historical experience and added value for achieving adaptation and mitigation in the scientific community, indicating a high occurrence probability in the future. Contrastingly, SSP5-RCP8.5 assumes an unfavorable world characterized by a strong reliance on fossil fuels and an economic-orientated emphasis with high forcing. This scenario serves as a comparison to underscore the perils of unbridled human activity. Table 1 lists the details of the main datasets, which are all freely available. All datasets were resampled at the resolution of 1 km for output by using the resampling method of nearest neighbor, since it will not alter the value of the input cells. According to the alignment of the center of cells, all datasets are rasterized and overlapped for spatial analyses.
## 4 Results and Discussion
### Effect of Coarse Aggregate Content  
#### 4.2.1 Experimental Results  
The depth of chloride ion diffusion in the concrete with different aggregate volume fractions when the hydrostatic pressure was 0.5 MPa can be seen in Figure 5. Specifically, when the volume fraction of the aggregate was 0%, the chloride ion diffusion depth was 20.9 mm; when the volume fraction of the aggregate was 20%, the chloride ion diffusion depth was 15.3 mm; when the volume fraction of the aggregate was 40%, the chloride ion diffusion depth was 13.5 mm; and when the volume fraction of the aggregate was 60%, the chloride ion diffusion depth was 12.6 mm. In addition, the chloride ion diffusion depths of the concrete with different aggregate volume fractions under different hydrostatic pressures are shown in Table 9.  
Figure 5: Test results of chloride ion diffusion under different volume fractions of the aggregate under a hydrostatic pressure of 0.5 MPa.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Hydrostatic Pressure: 0 MPA**} \\ \hline \multirow{3}{*}{**Number**} & \multirow{3}{*}{**First Group**} & \multirow{3}{*}{**Second**} & \multirow{3}{*}{**Third**} & \multirow{3}{*}{**Experimental Value**} & \multirow{3}{*}{**Simulation Value**} & \multirow{3}{*}{**Error** (\%)**} \\  & & & & & & \\ \cline{3-5}  & & & & & & \\ \hline S-0.35 & 7.2 & 8.1 & 7.0 & 7.4 & 8.0 & 7.6 \\ S-0.45 & 8.9 & 9.4 & 9.6 & 9.3 & 9.9 & 6.4 \\ S-0.55 & 12.3 & 12.7 & 13.7 & 12.9 & 12.1 & 6.2 \\ \hline \multicolumn{6}{c}{**Hydrostatic Pressure: 0.5 MPa**} \\ \hline \multirow{3}{*}{**Number**} & \multirow{3}{*}{**First Group**} & \multirow{3}{*}{**Second**} & \multirow{3}{*}{**Third**} & \multirow{3}{*}{**Experimental Value**} & \multirow{3}{*}{**Simulation Value**} & \multirow{3}{*}{**Error** (\%)**} \\  & & & & & & \\ \cline{3-5}  & & & & & & \\ \hline S-0.35 & 10.3 & 12.1 & 11.6 & 11.3 & 12.1 & 6.7 \\ S-0.45 & 15.9 & 16.3 & 17.9 & 16.7 & 15.5 & 7.1 \\ S-0.55 & 19.1 & 18.4 & 20.7 & 19.4 & 18.5 & 4.6 \\ \hline \multicolumn{6}{c}{**Hydrostatic Pressure: 1 MPA**} \\ \hline \multirow{3}{*}{**Number**} & \multirow{3}{*}{**First Group**} & \multirow{3}{*}{**Second**} & \multirow{3}{*}{**Third**} & \multirow{3}{*}{**Experimental Value**} & \multirow{3}{*}{**Simulation Value**} & \multirow{3}{*}{**Error** (\%)**} \\  & & & & & & \\ \cline{3-5}  & & & & & & \\ \hline S-0.35 & 16.1 & 15.3 & 17.3 & 16.2 & 17.5 & 7.8 \\ S-0.45 & 18.9 & 19.1 & 20.6 & 19.5 & 20.8 & 6.5 \\ S-0.55 & 26.7 & 26.3 & 23.7 & 25.6 & 24.1 & 5.7 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Test and simulation results for the cement mortar.
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 3 Dual-Code Group Network Plan Diagram Model with Time Line
### Calculation of Time Parameters
The single time difference \(r^{(s,f)}\) in a mission \((s,f)\) refers to the maximum magnitude by which the start of the mission can be delayed without affecting the earliest start time of its post-mission, which is represented as  
\[r^{(s,f)}=\min_{(f,k)\in\mathcal{F}\mathcal{M}_{(s,f)}}\left\{T_{ES}^{(f,k)}- T_{EF}^{(f,k)}\right\} \tag{13}\]  
Equation (10) gives the feasible range of plan start time and plan end time for each mission, which can guide the schedule of the mission. We make the plan start time of mission \((s,f)\)\(T_{PS}^{(s,f)}\) and the plan end time \(T_{PF}^{(s,f)}\). \(T_{PS}^{(s,f)}\) can be any value in the following range  
\[T_{PS}^{(s,f)}\in\left[T_{ES}^{(s,f)},T_{LS}^{(s,f)}\right] \tag{14}\]  
Once \(T_{PS}^{(s,f)}\) is determined, \(T_{PF}^{(s,f)}\) is similarly determined, i.e.,  
\[T_{PF}^{(s,f)}=T_{PS}^{(s,f)}+x^{(s,f)} \tag{15}\]  
Based on Equations (14) and (15), we can obtain a deterministic and executable plan that satisfies the mission cooperative relationships.
# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
## 4 Experiments
### Simulation
#### 4.1.3 Performance with Respect to Noise on Image Point  
Pixel noise on corner points of image is another error source during measurement. In this experiment, we investigate the sensitivity of our method with reference to pixel noise. Zero mean Gaussian noise is added to the pixel of corner points on the chessboard. The standard deviation \(\sigma\) of noise varied from 0.1 pixel to 1 pixel. Similar to Section 4.1.2, 300 independent tests are carried out for each noise level. The absolute errors of rotation  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Camera Value** & **LiDAR Value** \\ \hline \(f_{u}/\text{(pixel)}\) & 1200 & 300 \\ \(f_{v}/\text{(pixel)}\) & 1200 & 300 \\ \(u_{0}/\text{(pixel)}\) & 640 & 320 \\ \(v_{0}/\text{(pixel)}\) & 512 & 240 \\ Focus/mm & 24 & 6 \\ Resolution/(pixel) & 1280 \(\times\) 1024 & 320 \(\times\) 240 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of virtual camera and flash light detection and ranging (LiDAR).  
Figure 4: The relation between absolute errors and point cloud noise level: (**a**) absolute errors of rotation and (**b**) absolute errors of translation.  
\(e(\mathbf{R})\) and translation \(e(T)\) are shown in Figure 5. From the boxplot, we can observe that the errors of rotation and translation are linear with pixel noise level. The errors caused by pixel noise are smaller than errors caused by point cloud noise. Even \(\sigma=1\) pixel, the average error of rotation is about 0.16\({}^{\circ}\), and the average error of translation is about 2.7 mm.
# Whispered Speech Recognition Based on Audio Data Augmentation and Inverse Filtering
## 2 Whispered Speech  
Whisper represents a unique mode of verbal expression that, based on its distinct characteristics, nature, and way of production, deviates considerably from normal speech. As previously stated, the key features of whispering include the lack of a fundamental frequency and the noisy excitation of the vocal tract. Formant frequencies of whispered vowels are significantly greater than those of the neutral voice [19]. Compared to normal speech, whispered speech exhibits reduced frame energy, extended speech and silence durations, a flatter long-term spectrum, and a lower Sound Pressure Level (SPL).  
Even though a greater level of concentration is required for understanding whispered speech, its intelligibility remains remarkably high. An average identification accuracy of 82% in identifying vowels within [hVd] syllables when spoken in a whisper is demonstrated in [20]. Conversely, non-verbal cues such as age, sex, emotional state, and identity are often difficult to discern in whispered speech.  
Figure 1 illustrates the waveform and spectrogram of the phrase \"Govor sapata\" (\"Whispered speech\" in English) in Serbian. A phonetic transcription is also provided for normal speech (in capital letters) and whisper (in small letters). The amplitude levels in Figure 1a display a noticeable difference between the two modes of speech due to a loss of sonority. Additionally, the spectrogram in Figure 1b reveals the following characteristics:  
* Fricatives such as /s/ (i.e., /f/ in IPA notation) and plosives /p/ and /t/ are well preserved in whisper.
* The vibrant /r/ exhibits a similar shape in both normal and whispered speech.
* The absence of vertical lines representing glottal pulses shows that a harmonic structure of vowels is not present in a whisper.
# Evaluation of a Novel Condition Indicator with Comparative Application to Hypoid Gear Failures
## 2 Test Methodology  
### Application  
The testing applied here was intended to investigate the fatigue lifetime of an automotive differential hypoid gearset. The conditions were selected to accelerate the test while promoting gear tooth root bending fatigue failure modes.  
Differential gears play a universal and important role for automotive powertrains, almost independent of the vehicle type and drivetrain system. Hypoid gears are widely used in this context, to drive the differential gear and hence are an integral element of the drivetrain. Hypoid gears are a special group of bevel gears, with a hypoid offset, which means that the axes of the gears are crossed but not intersected. The offset of the gears can be used to optimize the packaging of the drivetrain of the vehicle while also affecting the gear face width and sliding within the contact. The definition of this offset, depending on the helix direction, and the representation of hypoid gears is depicted in Figure 1.  
One main advantage of hypoid gears is the high load-carrying capacity, as in the case of pinion gears, allowing a small number of teeth and a high ratio. With the high transferred loads and good NVH behavior, this gear type provides a compromise between bevel gear and worm gear designs. However, because of the special form of the geometry, rolling and sliding occur as a relative motion while meshing, which leads to losses, reducing the efficiency and increasing the risk of flank wear, pitting, scuffing and spalling [20].  
To fully utilize the advantages of hypoid gears without introducing significant losses, a very precise design and macro geometry correction is needed, which is still commonly based on \"experience\" with a manual variation of parameters [21]. Moreover, the contact pattern, important for the NVH behavior and for the fatigue life, varies with an increasing load because the relative positions of the pinion gear and the ring gear change, due to the stiffness of the bearings and housings in the assembly. This relative position can be defined by 4 variables, depicted in Figure 2 and represented by the letters E, P, G, \(\alpha\). P is defined as the axis of the pinion, G as the axis of the gear, E is the shortest distance between the the P and G axes and \(\alpha\) is the angle between P and G about the E axis. Prefixing any of these terms with \(\Delta\) indicates the difference in the relative ring gear and pinion positions compared to the unloaded state.  
Figure 1: Definition of hypoid offset.  
Figure 2: Definition of E, P, G, \(\alpha\)[21].  
These parameters (EPGa) are not fixed but variable with torque, speed and temperature. Additionally, wear during operation leads to an evolution of the contact pattern, micro-geometry and reaction forces over time, further complicating the system response.
## 3 Numerical Simulations Based on Matlab/Simulink for the Proposed PMSM-SCS Using DTC Strategy
### Numerical Simulation for PMSM-SCS Based on DTC Strategy Using Variable Flux
For the PMSM with the parameters given in Table 1, the graphical representation of the FD of the speed signal for the PMSM-SCS based on the DTC strategy is shown in Figure 17. So, for the constant flux case, Figure 17 depicts the FD of the speed signal for the PI speed controller, Figure 17 depicts the FD of the speed signal for the PI-EOA speed controller, Figure 17 depicts the FD of the speed signal for the FOPI speed controller, Figure 17 depicts the FD of the speed signal for the TID speed controller, Figure 17 depicts the FD of the speed signal for the TID speed controller, and Figure 17 depicts the FD of the speed signal for the SOC-type controller and Figure 17 depicts the FD of the speed signal for the FOSMC-type controller. It can be seen that the FD of the speed signal increases according to the controllers, which bring performance improvements to the closed-loop PMSM-SCS, based on the DTC. Figure 16: Comparison of speed evolution of PMSM-SCS based on DTC strategy using FO-Lead-Lag speed controller at constant flux and SMC- and FOSMC-type controllers at variable flux. Figure 15: Flux waveforms for PMSM-SCS based on DTC strategy using FOSMC-type controller at variable flux: (**a**) Flux space-vector trajectory; (**b**) Flux reference evolution. The usual performance of the PMSM-SCS based on the DTC strategy for the controllers shown in the previous sections is summarised in Figure 13.0 In addition to FD, time response and settling time, and speed ripple are shown [41]. Also, the usual performance indicators of a control system, namely overshoot and steady-state error, have not been included in Table 2, because for all the controller types presented, except the classic PI speed controller, the overshoot is negligible and the steady-state error is less than 0.1%. Improvements in the performance of the PMSM-SCS, based on the DTC strategy, are obtained when using a classical PI speed controller, when using an EOA-type algorithm and when using FO-type controllers, i.e., FOPI, TID, and FO-Lead-Lag speed controllers, at constant flux. In the case of variable flux, the SMC controller and the FO controller, i.e., the FOSMC controller, provide the best performance of the PMSM-SCS, based on the DTC strategy. Figure 13.1 Graphical representation of the FD of the speed signal for the PMSM-SCS, based on DTC strategy for the case of constant or variable flux: (**a**) PI controller at constant flux; (**b**) PI-EOA controller at constant flux; (**c**) FOPI controller at constant flux; (**d**) TID controller at constant flux; (**e**) FO-Lead-Lag controller at constant flux; (**f**) SMC-type controller at variable flux; (**g**) FOSMC-type controller at variable flux.
## 3 Confounding Adjustment Methods
### Traditional Regression  
For traditional regression, a model is fit for the response \(Y\) on the treatment indicator \(A\), covariates \(X\), and sometimes their interactions \(A*X\). For example, a multiple linear regression for a continuous endpoint is set up as  
\[\mathrm{Y}_{i}=\beta_{0}+\beta_{1}A_{i}+\beta_{2}X_{1i}+\beta_{3}X_{2i}+\cdots +\varepsilon_{i},\]  
where \((\beta_{0},\ \beta_{1},\beta_{2},\ \ldots)\) are the regression coefficients and \(\varepsilon_{i}\) is the random error. The least square estimator of \(\beta_{1}\) can be taken as the estimator of CATE.  
A logistic regression for a categorical endpoint is constructed as  
\[\mathrm{logit}[\mathrm{Pr}(\mathrm{Y}_{i}|A_{i},X_{i})]=\beta_{0}+\beta_{1}A_ {i}+\beta_{2}X_{1i}+\beta_{3}X_{2i}+\cdots.\]  
The difference \(\mathrm{Pr}(\mathrm{Y}=1|A=1,\,X=x)-\mathrm{Pr}(\mathrm{Y}=1|A=0,\,X=x)\) can be used as the estimator of CATE when \(X=x\).  
One concern of fitting a logistic regression is that the number of covariates may be very large compared to the number of events when the event of interest is rare. The rule of thumb is to have at least 10 events of the endpoint for each covariate in the regression, but that rule can be relaxed [36]. The regression model further assumes that the treatment effect measure is constant across the levels of covariates (or confounders) included in the model, but this is not often expected to be the case. Model mis-specification may lead to bias and impact precision in unbalanced designs with treatment effect heterogeneity [37]. With non-linear models, like logistic regression, the coefficient of treatment may not represent the marginal effect due to non-collapsibility [38]. For parameters such as odds ratios (OR), the subgroup-specific conditional treatment effects may be different from the unconditional treatment effect, even in the absence of confounding bias.