esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of a Simple Inerter in Seismic Base Isolation
## 2 Base-Isolated Rigid Structure with TMDI and ADAS
### Supplemental ADAS Device  
Figure 3 shows that the ADAS is responsible for the decrease in displacement of the base-isolated structure with optimal TMDI in the first cycles [37]. It is to be further noted that TMDI in base-isolated structures will be ineffective when subjected to near-fault pulse-type earthquake ground motions [41]. Under this condition, the response of the isolated structure is governed by the pulses associated with the earthquake record, and the tuning effects of TMDI will be unachievable. In view of the above facts, in place of the TMDI, it is proposed that the ADAS be added parallel to the isolation system, which may provide better control (refer to Figure 1c). For a specified inertance, the corresponding stiffness (\(k_{t}\)), and damper (\(c_{t}\)) of the 'optimal TMDI', constitute the ADAS in the present study. This is merely a comparison; additional optimal ADAS exploration can be conducted to achieve the desired response control of the base-isolated structures.  
A comparison of the normalized displacement variance of the base-isolated structure with the optimal TMDI and corresponding ADAS is depicted in Figure 4. The plotting of the response is done for the damping ratio, \(\xi_{b}\) = 0.05, 0.1, 0.15, and 0.2, and the isolation period, \(T_{b}\) = 3 s. The figure reveals that for the lower inertance ratios, the normalized variance of base-isolated TMDI is lower than the corresponding ADAS. However, for the higher inertance ratio, the ADAS is superior to the TMDI in reducing the displacement variance of the base-isolated structure. This can be explained by the fact that while the TMDI's performance is unaffected by higher inertance ratios, the amount of auxiliary damping (i.e., \(c_{t}\)) needed to achieve the best results is higher [40]. The performance of the isolated structure in displacement reduction improves when this damper with high damping as ADAS is attached directly to the ground and isolation floor. This figure also indicates that the reduction in displacement by the TMDI is more significant for isolated structures with lower damping in comparison to higher damping and confirms the recent finding [40].
## 2 Materials and Methods
### Experimental Design and Data Collection  
Nine growing zones were created in the growth chamber with three zones assigned randomly to each of the three light treatments (3 treatments, 3 replications). Forty-five potted plants were randomly placed into nine zones (five plants each).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Wavelength (nm)** & **300–400** & **400–500** & **500–600** & **600–700** & **700–800** \\ \hline \multirow{2}{*}{PFD} & UV (\%) & B (\%) & G (\%) & R (\%) & FR (\%) \\  & \(0.51\pm 0.18\) & \(17.9\pm 0.86\) & \(41.1\pm 0.42\) & \(35.6\pm 0.44\) & \(4.7\pm 0.64\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average light quality composition for the three light intensity treatments under fluorescent (4100 K) fixtures, the percent photon flux density (PFD) is presented for ultraviolet (UV), blue (B), green (G), red (R), and far-red (FR), on 100 nm increments.  
Figure 1: Photosynthetic active radiation (400 to 700 nm) spectral distribution graph for cool white fluorescent (4100 K) used for the three light intensity treatments. Spectral distribution was normalized with max 1.0.  
Every week, crown diameter, number of leaves, stolons, and daughter plants were recorded for each stock plant. After 12 weeks (9 weeks of daughter plant production period), all stolons were cut from the base. Daughter plants were excised from the stolons; and fresh mass (electronic scale), crown diameter (electronic caliper), leaf count (leaves greater than 1 cm), leaf area (LI-3100 Area Meter, LI-COR Inc., Lincoln, NE, USA), and dry mass (electronic scale after dried for 72 h at 65 \({}^{\circ}\)C) were measured. Daughter plants were grouped into six categories based on the crown diameter size (mm) (Figure 2) as follows: extra-large (XL) greater than 11, large (L) 8.5 to 11, medium (M) 6 to 8.5, small (S) 3.5 to 6 mm, extra-small (XS) smaller than 3.5, and premature (PM) daughter plant buds (Figure 2). In addition, dry mass was measured on each stolon without daughter plants. This first cycle was from week 1 to 12 (01 to 12 weeks) in this experiment.  
After removal of all stolons, the stock plants were returned to the growth chamber. The same parameters were measured for another 9 weeks (week 12 to 21). For weeks 12 to 21, all the measurements were the same as described above for weeks 01 to 12. In addition, at the end of week 21, stock plants were cut at the substrate level to quantify shoot fresh mass, leaf area, crown diameter, leaf count (>1 cm), and dry mass.  
All data were analyzed using JMP software (SAS Institute, Cary, NC, USA). Linear regression was used to model the plant response to light intensity and growing time. To identify the difference among treatments, significance level \(p=0.05\) was used. Tukey-Kramer HSD (\(p=0.05\)) was used when analyzed mean separation. No interactions were found between treatment and repetition in any of the growing cycles (weeks 01 to 12 and 12 to 21).
###### Abstract  
The penetration level of large-scale wind farms into power systems has been increasing significantly, and the frequency stability and transient stability of the power systems during and after a network fault can be negatively affected. This paper proposes a new control method to improve the stability of power systems that are composed of large wind farms, as well as usual synchronous generators. The new method is a coordinated controlling method between an adjustable-speed pumping generator (ASG) and a battery. The coordinated system is designed to improve power system stability during a disconnection in a fixed-rotor-speed wind turbine with a squirrel cage-type induction generator (FSWT-SCIG)-based wind farm due to a network fault, in which a battery first responds quickly to the system frequency deviation due to a grid fault and improves the frequency nadir, and then the ASG starts to supply compensatory power to recover the grid frequency to the rated frequency. The performance of the proposed system was confirmed through simulation studies on a power system model consisting of usual synchronous generators (SGs), an ASG, a battery, and an SCIG-based wind farm. Simulation results demonstrated that the proposed control system can enhance the stability of the power system effectively.  
adjustable-speed pumping generator; storage battery; power system stability; wind farm; FRT grid code +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Parametric Study on Hydrogen Purification Performance  
#### 4.3.1 Effect of Feed Pressure  
Figure 9 illustrates the variations in hydrogen purity and recovery under different feed pressures. During the AD step, an increase in pressure leads to greater adsorption of impurity gases. As a result, the molar fraction of hydrogen at the outlet increases, thereby enhancing purity. However, the increased adsorption amount also results in the higher consumption of feed gas, which in turn reduces the recovery. The figure further shows that as the feed pressure continues to rise, the rate of increase in hydrogen purity becomes more pronounced, particularly when the pressure is raised from 12 bar to 14 bar, where hydrogen purity increases from 97.36% to 97.98%. Simultaneously, the decrease in recovery becomes more significant. It should be noted that there is a limit to the effect of increasing pressure on hydrogen purity. Although this study does not address the effects at higher  
Figure 8: Variation in hydrogen purity and recovery obtained from the PSA cycle with the number of cycles (symbol: experimental data from Ref. [28]; line: simulation result).  
pressures, it is possible that beyond a certain threshold, further increases in pressure may have a diminishing impact on hydrogen purity. Nonetheless, within the pressure range studied, a higher feed pressure appears to improve hydrogen purity, albeit at the cost of a notable decrease in recovery.
## 4 Discussion  
In this narrative review, we summarize the main findings about the application of radiomics in HPV status prediction according to different imaging techniques.  
A growing body of evidence suggests that radiomics could help in the development of tailored therapy in HPV+ OPSCC, due to its contribution in determining HPV status, along with the standard assessment methods routinely adopted in clinical practice. The  
Figure 2: PRISMA flow-chart illustrating the various phases of the review search and the study selection process.  
use of radiomic features through the analysis of conventional imaging may be helpful in non-invasively stratifying patients according to their HPV status, thus improving the development of more personalized therapies. In most series, the effectiveness of radiomic models is evaluated using ROC curves and calculation of the AUC.  
Our conclusions are in line with those previously published by [PERSON] et al. [30]. In their review, they summarized the most recent advances in the application of radiomics in OPSCC for assessing HPV status, predicting survival outcomes and identifying relevant pathological features (such as lymphovascular invasion or extracapsular nodal spread or metastatic lymph node involvement).  
On the other hand, in 2022, [PERSON] et al. [10] conducted a systematic literature review with Radiomic Score Quality (RSQ) assessment focusing on the application of radiomics in OPSCC for HPV infection prediction. They selected 19 articles with a median RSQ of 33% and found that radiomics and machine learning studies for the assessment of HPV have shown low overall quality. They therefore recommended higher methodological quality, appropriate standardization and greater attention to model validation before clinical adoption.  
Although no direct comparison was made, MRI-based predictive radiomic models showed similar performance when compared to CT-based ones [11]. This suggests that Contrast-Enhanced (CE) T1-weighted MRI and CT might reveal similar textural properties relevant for the discrimination of HPV-positive and HPV-negative disease, even if which technique is the most appropriate is still under investigation. MRI could outperform CT imaging for staging and radiomic analysis due to its better soft tissue contrast; however, the choice will depend on the confidence and experience of the radiologists within the center. On the other hand, the 18F-FDG PET-based radiomic model slightly underperforms compared to MRI and CT. This can be attributed to the non-morphological nature of this imaging technique, which is less able to provide textural detail of tumor tissue.
## 5 Test Results and Analysis
### Algorithm Training Results  
First, the input is divided into a training set and validation set into the YOLOv7 and YOLOv5s algorithm networks for training. After 200 epochs of training, the positioning loss and confidence loss function value curves during training are shown in Figure 9, including the detection box loss and detection object loss. The abscissa represents the number of iterations, and the ordinate represents the loss value.  
The box loss curve shown in Figure 9 shows that the box loss value of the YOLOv7 algorithm decreases rapidly between 0 and 50 of the training batch, and then the decline rate becomes slow. After 200 epochs of training, the box loss value of YOLOv5s algorithm on the training set is slightly larger than the box loss value of the YOLOv7 algorithm. The box loss value of the YOLOv7 algorithm on the validation set is greater than that of the  
Figure 8: Centroid of _Camellia oleifera_ fruit. (**a**) Median filtering, (**b**) Mask extraction, (**c**) Canny, (**d**) Centroid.  
Figure 9: Loss function value curve.  
YOLOv5s algorithm, and finally stabilizes at around 0.06. The object loss curve shown in Figure 9 demonstrates that the object loss value of the YOLOv7 algorithm decreases during training, and the YOLOv5s algorithm shows a trend of rising first and then declining. The object loss value of the YOLOv7 algorithm is less than that of the YOLOv5s algorithm on the validation set, and the final loss value tends to be stable at about 0.015.
# A Survey of the Effects of Vehicle Configuration on Urban Air Mobility +
## 2 Aerial Urban Vehicle Configurations and Concepts
### Vertical Take-Off and Landing (VTOL)
#### 2.2.2 Multirotor
In the case of multirotors, the two most prominent system faults that could occur during the flight are: single or multiple engine failures and damages to the airframe. The former results in a shift in the multirotor's center of thrust affecting both static stability as well as the flight performance while the latter causes a shift in the aerodynamic center which in this configuration, for the most part only affects the flight performance [51]. Due to the control the redundancy that is often present in multirotors, the occurrence of such faults in the system can often be addressed through the employment of control allocation algorithms. In such an approach, the fault configuration in the system would first be found using a fault detection module, then the relationship between the control system torque commands and rotor speed would be adjusted in order to maintain stability and restore flight performance quality to some extent [24; 52]. In order for the 6 DOF performance to be fully restorable through control allocation in case of single-engine failure, the multirotor needs to at least have eight rotors [53]. However, the performance of a multirotor with fewer rotors can be guaranteed in the presence of partial faults in actuators [54]. Research on quadrotors in the presence of a full actuator fault has proven the feasibility of stabilization of the aircraft using fault-tolerant controllers [55]. Furthermore, in some cases, faults can also be addressed by shifting the aircraft's center of mass if possible [56].
# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## 6 Numerical Experiments  
We conducted a numerical study to investigate the performance of our optimization problems. For each optimization problem, we conducted two sets of experiments. The first set included only periodic traffic (\(\sum_{i\in\mathcal{T}}x_{k}^{i}\geq 1\), \(\hat{h}(k)=\hat{h}(k)=0\), \(k\in\mathcal{K}\)), while the second set included both periodic and rate demand traffic (\(\sum_{i\in\mathcal{T}}x_{k}^{i}\geq 1\), \(\hat{h}(k),\hat{h}(k)\geq 0\), \(k\in\mathcal{K}\)). We varied the number of nodes from 4 to 32, using only multiples of 2 to facilitate the experiment design, as will be explained further below. We set the maximum number of slots to 15, as this gave us some instances with the number of nodes greater than the maximum frame length, and some less, as well as in practice giving reasonable experiment run times. The details of each set of experiments are given below and are summarized in Table 3, and a summary of the experimental parameters is given in Table 4. For each case, we generated and solved 10 problem instances, with periodic and/or background traffic for each uniformly randomly generated from the ranges given in Table 4.  
Each formulation was implemented in AMPL and solved using CPLEX on an Intel Xeon E5-2420 CPU with 12 cores and 24 GB of RAM. Our AMPL code, input data for each experiment, and Python code to generate the input data, run the optimizations, and process and plot the results are available online [34].  
\begin{table}
\begin{tabular}{c l c} \hline \hline
**Symbol** & **Description** & **Value** \\ \hline \(S\) & total number of slots & 15 \\ \hline \(K\) & number of nodes & 4, 8, 16, 32 \\ \hline \(D\) & period (slots) & Short: 2–10, Long: 11–20 \\ \hline \(h\) & background traffic rate (transmissions per slot) & low: 0.05–0.1, high: 0.1–0.5 \\ \hline \(C\) & coherence interval (ms) & 1 \\ \hline \(p\) & maximum number of pilots & 16 \\ \hline - & solver timeout & \(10,800\) s (30 h) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment** & **Periodic Traffic** & **Background Traffic** \\ \hline
1A & Short & None \\ \hline
1B & Long & None \\ \hline
1C & Mixed & None \\ \hline
2A & Short & Low \\ \hline
2B & Long & High \\ \hline
2C & Mixed & Mixed \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiments.
## 2 Materials and Methods
### Methods
#### 2.4.2 Optimal Probability Distribution Model for Effective Antecedent Precipitation  
By examining the recurrence patterns of historical precipitation events, the potential for future precipitation-induced landslides can be assessed [55; 56]. Eight kinds of maximum 10 day EAP probability distribution models are used in this research: normal Gaussian, lognormal, gamma, generalized extreme values, Rayleigh, Weibull, exponential, and Pearson type III, each of which is tested to determine its fitness at each meteorological station. In this framework, three validation methods are used for finding the optimal distribution function: the Akaike information criterion (AIC), the Kolmogorov-Smirnov test, and the root mean square error (RMSE), allowing us to calculate EAP at different return periods for each site. These methods jointly evaluate model performance from two perspectives: goodness of fit and predictive accuracy, thereby supporting the selection of the optimal distribution for each station. In cases where the three methods yield different results, a majority-voting strategy was adopted; i.e., the distribution supported by at least two of the three criteria was selected. When no consensus could be reached, due to each  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Precipitation** & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.8** & **0.9** & **1** \\ \hline
**Pearson** & 0.3093 & 0.3130 & 0.3168 & 0.3207 & 0.3247 & 0.3288 & 0.3325 & 0.3352 & 0.3348 & 0.3278 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlation analysis of the precipitation coefficient.  
criterion favoring a different distribution, RMSE was used as the final determining metric, as it more directly reflects the predictive error in estimating antecedent effective precipitation.  
The AIC, a statistical method for model selection introduced by [PERSON] [57], is expressed as  
\[\text{AIC}=2k-2\text{ln}\left(\dot{L}\right) \tag{6}\]  
where \(k\) is the number of parameters in the model and \(\dot{L}\) is the likelihood estimate.  
In statistical analysis, the Kolmogorov-Smirnov test is a significant nonparametric method for comparing the distributions of two samples [58]. The statistic \(D\) represents the Kolmogorov-Smirnov test statistic, which calculates the maximum difference between the cumulative distribution functions (CDFs) of two samples, and is defined as  
\[D=\text{ max}_{\text{x}}|F_{1}(x)-F_{2}(x)| \tag{7}\]  
where \(F_{1}(x)\) and \(F_{2}(x)\) are the CDFs of the two samples.  
The spatial distribution of EAP was fitted using the ANUSPLIN interpolation method, a commonly used interpolation technique developed by the Australian National University [59; 60]. This method effectively balances interpolation accuracy and smoothness, combining topography and climate for accurate interpolation [61].
# Slip Estimation Model for Planetary Rover Using Gaussian Process Regression
## 7 Conclusions  
This paper developed slip estimation models for the Mars rover Zhurong using GPR and GA-BP. The training data came from ground tests on two simulated soils, TYII and JLU Mars-2. The data also had higher accuracy and sampling frequency. The models were validated using Zhurong data on Mars conditions, and the results demonstrated that the soil type and dataset source had a direct impact on the applicability of the slip model on Mars conditions. The properties of the Martian soil near the Zhurong landing site were  
Figure 12: Planning path, predicted path, and deviation from sol.27–sol.36: (a) sol.27; (b) sol.28; (c) sol.34; (d) sol.36; (e) sol.40.  
most similar to the JLU Mars-2 simulated soil. This demonstrated the necessity of using simulated Martian soil for ground tests, which has not been not considered in the existing literature. The GPR models had high estimation accuracy, and were able to show the slip behavior of the rover. These models offered a 95% confidence interval. Thus, this work offered a basic research direction for subsequent slip-based path planning and path tracking. There are some limitations of our work; only two kinds of simulated soils were used in the test, thus it is hard to deeply explore the correlation between soil properties and slip behavior. Moreover, the training of the Gaussian process regression model takes a long time, and the algorithm needs to be improved.  
Conceptualization, Y.J. and T.Z.; software, S.P. and J.S.; data curation, H.T. and J.S.; writing--original draft preparation, T.Z.; supervision, C.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors are grateful to the Chinese Academy of Space Technology for test and data supporting.  
The authors declare no conflict of interest.
## 2 Autonomous Global Positioning Method  
An autonomous global positioning system based on the Earth's sky polarized light and a true north measurement instrument generally includes several polarized light sensors, a true north measurement device, and a computer. The global position calculation flowchart of an autonomous global positioning system is presented in Figure 1.  
The specific steps of the global position calculation flowchart are as follows. First, a polarization light sensor measures the scattered light in the atmosphere to obtain a polarization vector \(\mathbf{e}\). The orthogonal vector algorithm [24] obtains a solar vector \(\mathbf{S}_{b}\) in the body coordinate system. Then, the three altitude angles provided by a true north measurement instrument are used to determine a solar vector \(\mathbf{S}_{n}\) in the East-North-Up (ENU) coordinate system. Next, the solar altitude angle and the solar azimuth angle are calculated. Further, the Coordinated Universal Time (UTC) is obtained from the clock integrated into a computer; this allows for the retrieval of the corresponding solar declination and solar time difference based on the UTC and the astronomical calendar. Finally, this information is substituted into the system of equations of a global positioning method to obtain the global position of the carrier.