esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Assessment of Student Satisfaction  
An analysis was carried out through a satisfaction survey to evaluate the satisfaction of teaching based on virtual reality. For teaching, the attained results show that the virtual world can be quite efficient in time, and the students evaluated the activity positively. The results of the satisfaction questionnaire are summarized in Table 4. Students were asked to provide their feedback related to the design and their viewpoint about the methodology and acquired skills through the used tool. Also, they were asked to provide a grade from 1 to 5, 5 being the best. Regarding the virtual world, 95.8% of the respondents agree that the exhibited virtual environment has no differences between the facilities of the faculty and the virtual world, except that the faculty does not have the photovoltaic system installed, however, the equipment is very similar to the real world. Students agreed with the rendering of the objects; individually, users suggested an improvement to the inverter textures was important, although this was not significant enough to generate confusion on the technical planning.  
Most students think they could understand most textual descriptions and learned about the presented details in the virtual plant. They agree that the virtually designed scenes have adequate contents provided them with useful knowledge. The statistical results reveal that the scores, in this category, lie between 3.88 and 4.32, while the overall average score is 4.12.  
The virtual laboratory allowed them to understand and visualize all the technical details of the installation. However, some of them report feeling a bit dizzy after using the Oculus, and those who have visual problems were found to be uncomfortable with the Oculus as expected. Others say: \"I found a class like this much more practical since you can understand and interact not only with the diagrams, but also with the real connections\", and \"It is a handy tool since it helps you to interact with the diagrams and understand the installations.\"  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Questionnaire** & **Score** \\ \hline
1. Is the V world similar to the facilities at the faculty? & 4.83 \\
2. Are the virtual objects look like in reality? & 4.91 \\
3. Is it easy to operate the virtual environment? & 4.91 \\
4. Is the text clear in the VR? & 4.75 \\
5. Is the audio appropriate to the explanation during the virtual tour? & 4.66 \\
6. Using virtual labs enhances my understanding of the theory Classes? & 4.75 \\
7. Using virtual labs improves my professional practical skills? & 4.75 \\
8. The use of a world is useful for the teaching–learning of installing medium scale photovoltaic system & 4 \\ Average & 4.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: User satisfaction survey.  
Figure 9: Knowledge improvement measurement.
## 4 Discussion
### Why Red SIF Tracks GPP Better Than Far-Red SIF under Low Air Temperature
This may be explained by the fact that low temperature inhibited the PSII activity, and the electrons transferred more to the PSI, which could result in the enhancement of the PSI activity, and the magnitude of the \(tSIF\) from PSI might also increase. Therefore, the increase in the \(tSIF\) from PSI could be compensated for the decline in the \(tSIF\) from PSII, especially at the far-red band. However, the PSII portion of the red \(tSIF\) might dominate the red SIF, and thus the red SIF quantum yield at the photosystem level (\(\Phi_{F\_688}\)) might be likely to decline in the overwintering period. [PERSON] al. [30] explored the apparent spectral fluorescence yields (\(ASIF_{yield\_687}\) and \(ASIF_{yield\_760}\), which were measured by SIF normalized with PAR) for wheat in correlation with GPP and found that \(cSIF_{760}\) related more strongly to GPP than \(cSIF_{688}\). It should be noted that \(cSIF_{760}\) carries much information relating to the canopy structure. [PERSON] et al. [30] only explored the GPP estimation based on canopy SIF. In our study, we used ANCOVA analysis to explore the difference between \(\Phi_{F\_688}\) and \(\Phi_{F\_760}\) and found that they had a significant difference (\(p\)-value \(<0.05\); Figure S15 and Table S5). Therefore, we could infer that the different changes in \(\Phi_{F\_760}\) and \(\Phi_{F\_688}\) in response to temperature may result from the SIF at the red band, and the far-red band contains different contributions from both PSI and PSII.
# Measurement of Head-Related Transfer Functions: A Review
## 2 Principle of HRTF Measurements
### Golay Codes  
Golay codes [52] are a pair of complementary sequences, \(\{a_{\text{L}},b_{\text{L}}\}\), with a length of N = 2\({}^{\text{L}}\) (L is the sequence order). The pair of complementary sequences is initialized for example as (L = 1): \(a_{1}=\{1,1\}\) and \(b_{1}=\{1,-1\}\). For L \(\geq\) 2, the Golay codes are generated according to the following recursion rules [35, 55]:  
* \(a_{\text{L}}\): Append \(b_{\text{L}-1}\) to \(a_{\text{L}-1}\)
* \(b_{\text{L}}\): Append \(-b_{\text{L}-1}\) to \(a_{\text{L}-1}\)  
After L\(-\)1 recursive processes, a pair of Golay codes, \(\{a_{\text{L}},b_{\text{L}}\}\), with a length of 2\({}^{\text{L}}\) is obtained. An important property of Golay codes is that the sum of the auto-correlations of the pair of complementary sequences is zero except at the origin (two-valued function) [66]. In the frequency domain, this property is expressed as:  
\[\text{FFT}(a_{\text{L}})\ \text{FFT}^{*}(a_{\text{L}})+\text{FFT}(b_{\text{L}}) \ \text{FFT}^{*}(b_{\text{L}})=2\text{N}, \tag{8}\]  
where FFT\({}^{*}\) stands for the complex conjugates of FFT.  
This property can be utilized for the measurement of the acoustic transfer function \(H(f)\) as shown in Figure 5: \(a_{\text{L}}\) and \(b_{\text{L}}\) are separately emitted from the loudspeaker, the recorded signal is transformed in the frequency domain using an N-point FFT, resulting in \(H(f)\) FFT(\(a_{\text{L}}\)) and \(H(f)\) FFT(\(b_{\text{L}}\)). After that, the first part, \(H(f)\) FFT(\(a_{\text{L}}\)), and the second part, \(H(f)\) FFT(\(b_{\text{L}}\)), are multiplied by FFT\({}^{*}(a_{\text{L}})\) and  
Figure 4: An L-stage linear feedback shift-register for the generation of maximum length sequence (MLS) signals (adapted from Figure 2.1 in [35], published by J. Ross Publishing. All rights Reserved). \(a_{i}\) denotes the state of each register, where \(i\in\{1,2,3,...,\text{L}\}\).  
FFT\({}^{*}(b_{\rm L})\), respectively. The resulting two parts are summed together to obtain \(H(f)\) by applying Equation (8):  
\[H(f)=\frac{1}{2\text{N}}\ [H(f)\ \text{FFT}(a_{\rm L})\ \text{FFT}^{*}(a_{\rm L })+H(f)\ \text{FFT}(b_{\rm L})\ \text{FFT}^{*}(b_{\rm L})]. \tag{9}\]  
After that, the system impulse response \(h(n)\) is calculated by applying the IFFT to \(H(f)\). However, the two complementary sequences, \(\{a_{\rm L},b_{\rm L}\}\), must be excited sequentially (one after the other). For individual HRTF measurements, this approach is not robust to the time-variance effect caused by unconscious movements of subjects. In this sense, the MLS/IRS is preferable to Golay codes when measuring HRTFs for human subjects [35; 67].
## 2 Materials and Methods
### Data Analysis  
The deformation is defined as the surface variation of the investigated area. To reduce the effect of spatial decorrelation and errors induced by DEM inaccuracy, only interferograms with small orbital baselines were selected for subsequent time-series analyses. This method is based on the phase comparison of multiple SAR images gathered at different times over the study region and allows for measurements of land deformation along the LOS direction of the SAR sensor with up to millimeter precision [57]. In addition, a procedure is implemented to reduce the de-correlation effect caused by noise and avoid its negative impact when calculating the LOS offset. PS are artificial objects that reflect the radar signal well, such as metal structures, buildings, and rock outcrops, and are used in persistent scatterer interferometry (PSI), e.g., the permanent scatterers (PS-InSAR) technique [28; 40]. In urban areas, there is a prevalence of PS, and PSI methods allow for the analysis of even individual structures on the ground. PS can be geolocated more accurately, and a residual error of the residual DEM can be calculated after the initial subtraction of the DEM. Various types of PS can be distinguished from each other, and provide a way to locate points in the resolution cell [28; 58]. This type of processing reduces the atmospheric noise generated by conventional processing [46; 47]. Then, all PS points were generated following the method proposed in [52]. The vertical displacement (vd) was calculated in the following Equation (1).  
\[\text{vd}=-\text{LOS}\times 1/\text{cos}\theta \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Image ID** & **Satellite Sensor** & **Resolution** & **Acquisition Date** \\ \hline
1 & S1B\_IW\_SLC\_1 SSV\_20170115 & Sentinel 1-B & 5 & 15 January 2017 \\
2 & S1B\_IW\_SLC\_1 SDV\_20170208 & Sentinel 1-B & 5 & 8 February 2017 \\
3 & S1B\_IW\_SLC\_1 SDV\_20170304a & Sentinel 1-B & 5 & 4 March 2017 \\
4 & S1B\_IW\_SLC\_1 SDV\_20170409 & Sentinel 1-B & 5 & 9 April 2017 \\
5 & S1B\_IW\_SLC\_1 SDV\_20170503 & Sentinel 1-B & 5 & 3 May 2017 \\
6 & S1B\_IW\_SLC\_1 SDV\_20170608a & Sentinel 1-B & 5 & 8 June 2017 \\
7 & S1B\_IW\_SLC\_1 SDV\_20170702A. & Sentinel 1-B & 5 & 2 July 2017 \\
8 & S1B\_IW\_SLC\_1 SDV\_20170807 & Sentinel 1-B & 5 & 7 August 2017 \\
9 & S1B\_IW\_SLC\_1 SDV\_20170912 & Sentinel 1-B & 5 & 12 September 2017 \\
10 & S1B\_IW\_SLC\_1 SDV\_20171006 & Sentinel 1-B & 5 & 6 October 2017 \\
11 & S1B\_IW\_SLC\_1 SDV\_20171111 & Sentinel 1-B & 5 & 11 November 2017 \\
12 & S1B\_IW\_SLC\_1 SDV\_20171205 & Sentinel 1-B & 5 & 5 December 2017 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characterization of radar satellite images selected.  
where LOS is the unwrapped phase obtained by InSAR processing and \(\theta=37.32\) is the incidence angle [59].  
From the data analysis, it was possible to generate 12 deformation maps (a map by month) in the study area for the year 2017. All maps were created via the universal Kriging method [60, 61] in SAGA GIS 2.3.2 software [62].
###### Abstract  
Accurately estimating the energy consumption of unmanned aerial vehicles (UAVs) in real-world delivery scenarios remains a critical challenge, particularly when UAVs operate in complex urban environments and are coupled with public transportation systems. Most existing models rely on oversimplified assumptions or static mission p profiles, limiting their applicability to realistic, scalable drone-based logistics. In this paper, we propose a physically-grounded and scenario-aware energy sizing methodology for UAVs operating as part of a last-mile delivery system integrated with a city's bus network. The model incorporates detailed physical dynamics--including lift, drag, thrust, and payload variations--and considers real-time mission constraints such as delivery execution windows and infrastructure interactions. To enhance the realism of the energy estimation, we integrate computational fluid dynamics (CFD) simulations that quantify the impact of surrounding structures and moving buses on UAV thrust efficiency. Four mission scenarios of increasing complexity are defined to evaluate the effects of delivery delays, obstacle-induced aerodynamic perturbations, and early return strategies on energy consumption. The methodology is applied to a real-world transport network in Belfort, France, using a graph-based digital twin. Results show that environmental and operational constraints can lead to up to 16% additional energy consumption compared to idealized mission models. The proposed framework provides a robust foundation for UAV battery sizing, mission planning, and sustainable integration of aerial delivery into multimodal urban transport systems.  
U
## 3 Results
Box plots denoted with different letters significantly differ (HSD test) on a significance level of \(p<0.05\). Figure 4: Measured values of contact angle (CA) for all experiment treatments. The whiskers extend to a distance of 1.5 times the interquartile range from the edges of the box and points represent individual measurements. Box plots denoted with different letters significantly differ (HSD test) on a significance level of \(p<0.05\). Figure 3: Measured values of the saturated hydraulic conductivity (_Ks_) for all treatments of the experiment. The whiskers extend to a distance of 1.5 times the interquartile range from the edges of the box and points represent individual measurements. Box plots denoted with different letters significantly differ (HSD test) on a significance level of \(p<0.05\). The test results did not indicate interactions between the variables of pyrolysis temperature and particle size, with a \(p\)-value of 0.130 (Table 4). However, there is an interaction between pyrolysis temperature and application rate with a \(p\)-value of \(<\)0.001. Additionally, an interaction between the particle size and application rate was observed with a \(p\)-value of 0.001. No interaction was observed between the pyrolysis temperature, particle size, and application rate variables, with a \(p\)-value of 0.765 (Table 4). An HSD post hoc test confirmed significant differences in \(P\) between the treatment with the small-sized BC, pyrolyzed at 300 \({}^{\circ}\)C and applied at a rate of 40 Mg ha\({}^{-1}\), and all large-sized BCs (produced at both temperatures, applied at both rates). Also, there was a significant difference between the large-sized BC, pyrolyzed at 520 \({}^{\circ}\)C and applied at a rate of 40 Mg ha\({}^{-1}\), and all small-sized BCs produced at both temperatures and applied at both rates. All significant differences in \(P\) between experimental treatments are shown in Figure 1. Compared to the control, the increase in \(P\) was measured only in treatments with the large-sized BC (the highest increase was observed in treatment with the BC pyrolyzed at 520 \({}^{\circ}\)C and applied at a rate of 40 Mg ha\({}^{-1}\)).
## 5 Conclusions  
To obtain early warnings of creep landslides by automated monitoring, this paper proposes a hybrid warning method combining the conventional warning method based on cumulative deformation, velocity, and acceleration with the critical sliding warning method based on the normalized tangent angle. The application in the Lishanyuan landslide shows that the proposed method can cover the whole life cycle of landslide monitoring and can accurately grasp the accelerating deformation process of creep landslides while reducing false early warnings to the greatest extent.  
Considering the characteristics of automated monitoring data from landslides, such as large volume, periodic fluctuation, outliers, and unequal collection intervals, this paper proposes the concept of time windows and uses the data within time windows to calculate deformation parameters such as velocity, acceleration, and the normalized tangent angle by the least-squares method after preprocessing, which makes full use of monitoring data and reduces calculation errors.  
We also propose some methods for calculating the thresholds in the conventional early warning and the critical sliding warning method. These methods are more reliable than the empirical-based methods.  
Accurate grasp of each accelerating deformation process of a landslide is a prerequisite for the critical sliding early warning. The method proposed in this paper not only assists landslide monitoring practitioners in identifying a landslide's displacement acceleration process quickly and accurately, but also allows the selection of different strategies to adjust the allocation of computational resources according to different scenarios and needs,lowering the cost of monitoring and warning systems and being of great importance for future large-scale automated landslide monitoring.  
Conceptualization, D.B. and G.L.; methodology, D.B.; software, D.B.; validation, D.B. and J.F.; formal analysis, D.B.; investigation, D.B.; resources, Z.Z.; data curation, D.B.; writing--original draft preparation, D.B.; writing--review and editing, G.L., Z.Z., X.Z., C.T., J.F.; visualization, D.B., X.Z., C.T.; supervision, G.L.; project administration, G.L.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number: 41974148; Key research and development program of Hunan Province of China, grant number: 20205K2135; Natural Resources Research Project in Hunan Province of China, grant number: 2021-15; and Department of Transportation of Hunan Province of China, grant number: 202012.  
Not applicable.  
We would like to thank the editor and the reviewers for helping us improve the quality of the manuscript.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Chemometrics of the DES-Based Extracts
#### 3.2.3 Correlations between Antioxidant Capacity Methods  
The degree of linear association between the three analytical methods applied for AC measurements was determined using regression analysis and calculating Pearson correlation coefficient (r). The calculated r values presented in Figure 5 indicate that there are significant associations among AC results of all extracts determined by CUPRAC, ABTS, and DPPH assays (r ranged between 0.6466 and 0.8138, \(p=0.00000001\)-0.00002).  
These high r values demonstrated strong relationships between the ET-based CUPRAC method and the scavenging activity of antioxidants present in the analyzed extracts toward stable ABTSTS\({}^{\bullet+}\) cation radicals and DPPH\({}^{\bullet}\) radicals. Unexpectedly, the lowest r = 0.6466 for ABTS and DPPH can be explained by the fact that examined extracts obtained by six different DESs were not rich sources of antioxidants, which at the same time are capable of neutralizing ABTSTS\({}^{\bullet+}\) cation radicals and DPPH\({}^{\bullet}\) radicals.  
For comparison, a higher significant linear correlation (r = 0.979, \(p<0.001\)) between ABTS and DPPH results for extracts from the perennial herb _Blumea aromatica_ prepared using seven deep eutectic solvents (ChC1:LA (1:3), ChCl:AceA (1:3), ChCl:Eth (1:3), ChCl:U (1:3), ChCl:Ge (1:3), ChCl:Be (1:3), and ChCl:Oxa (1:2)) and three traditional solvents (ethanol, methanol, and water) was demonstrated by [PERSON] et al. [34]. In addition, statistical analysis revealed positive correlations (r = 0.4226-0.8603) between the AC determined by DPPH and FRAP tests and the total contents of phenolics and flavonoids in five extracts from Greek medicinal plants (dittany, fennel, marjoram, mint, and sage) prepared using LA-based DESs, EtOH (60%), and UAE techniques [24]. Similarly, ABTS values for ethanolic  
Figure 5: Correlation matrix (Pearson’s correlation coefficients) for the 36 studied extracts.  
extracts of chokeberry (_Aronia melanocarpa_) prepared by four different extraction methods (UMAE, UAE, MAE, and TSE) highly correlated with DPPH results (r = 0.9843) [26]. High correlation coefficients (0.813-0.906) between ACs determined by DPPH, ABTS, and FRAP assays were also found for water infusions of 87 medicinal herbs and spices [4]. Nevertheless, [PERSON] et al. [35] observed very strong correlations (r = 0.9514-0.9999) between CUPRAC, DPPH, and FRAP values and TPC in the water extract of _Melissa officinalis_ L. herb.
## 3 Results
### Correlation Analysis of NPP and Driving Factors  
The total NPP reflects the increase or decrease in the mean NPP; therefore, the total NPP value and the mean NPP value were used for analysis with the driving factors. The CO\({}_{2}\) spatial grid data were monthly data, and the mean values were calculated for each district and banner of Ordos City separately to obtain the annual mean CO\({}_{2}\) data. Pearson correlation analysis was performed on the annual mean CO\({}_{2}\) data and the annual total NPP data to explore the link between them. The data for gross regional product, gross industrial product, gross construction product, total population, agricultural population, non-agricultural population, fixed asset investment, total crop area sown, total grain production, total number of end-of-year livestock, total output value of agriculture, forestry, animal husbandry and fishery, total power of agricultural machinery, usable grassland area and raw coal production were obtained from the Ordos City Statistical Yearbook for correlation analysis with the total NPP. Annual mean precipitation and annual mean temperature data were also correlated with mean NPP for the analysis.  
As shown in Table 1, NPP is significantly correlated with CO\({}_{2}\) emissions from fossil fuel combustion, the total population, the non-agricultural population, fixed asset investment, usable grassland area, raw coal production and annual mean precipitation. Among them, the usable grassland area and annual mean precipitation show significant positive correlations with NPP, whereas other factors show more significant negative correlations. In descending order of influence: usable grassland area > non-agricultural population > emissions of CO\({}_{2}\) from fossil fuel combustion > annual mean precipitation > total population > raw coal production > fixed asset investment.  
Figure 5: Spatial change in NPP between 2000 and 2019.
# Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks
## 4 Proposed MAC for LLDN
### Network Model  
Consider a WSN of star topology, where \(N\) number of nodes, \(W_{0},W_{1},....,W_{N}\), are associated with the PAN coordinator. Each node uses the CSMA/CA channel access mechanism in the allotted shared slot for competing to transmit data frames to the coordinator. The coordinator follows the LLDN superframe structure as shown in Figure 1. The owner nodes associated with dedicated slots have a better chance for successful transmissions. Assume that nodes always transmit the most recent generated data to the coordinator, and the sensing range of a node is twice of the transmission range. Every node goes for the power saving mode after the scheduled transmission/receiving slots. A node defers the transmission during accessing the channel in a shared slot if the remaining time is not enough to transmit the data in the shared slot.