esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
Correlation between the Operative Angles of the Rig and the Orientation Angles of a Vehicle under Test  
Thanks to its two DoFs, i.e., the possibility to set the operative angles \(\alpha\) and \(\beta\), the test rig object of this study can easily impose a generic spatial orientation to the vehicle under test in terms of angles \(\alpha\) (of the support plane) and \(\beta^{\prime}\) (of the vehicle longitudinal axis with the maximum slope direction; \(\beta^{\prime}=\beta\) only when the vehicle longitudinal axis is parallel with \(y_{SP}\)) and hence of angles of roll \(\varphi\), pitch \(\delta\) and yaw \(\psi\) (Figure 3), according to the following formulas:  
Figure 2: The approach followed in the prediction of experimental outputs in the two operative modes for the test rig illustrated in this paper.  
\[\left\{\begin{array}{l}\varphi=\arctan\frac{\sin\alpha\cdot\tan\beta^{\prime}}{ \sqrt{1+\cos^{2}\alpha\cdot\tan^{2}\beta^{\prime}}}\\ \delta=\arctan\frac{\sin\alpha}{\sqrt{\tan^{2}\beta^{\prime}+\cos^{2}\alpha}} \\ \psi=\arctan\frac{\tan\beta^{\prime}}{\cos\alpha}\end{array}\right. \tag{1}\]  
In particular, if compared to conventional test rigs, the second degree of freedom of the presented rig (i.e., the possibility of rotation for the turntable) prevents experimenters from having to carry out maneuvers to re-position the vehicle at different angles of inclination of its longitudinal axis with respect to the maximum slope direction of the support surface. This has made possible to design two classes of stability tests (global completely static tests, global quasi-static tests) that are completely new if compared with the tests proposed so far (in which the vehicle has its longitudinal axis parallel or perpendicular to the axis of inclination of the supporting plane; see Table 1).  
If considering a perfect alignment of the vehicle frame of reference with the turntable frame of reference (in particular when the vehicle longitudinal axis is parallel to \(y_{SP}\)), the angles of roll (\(\varphi\)), pitch (\(\delta\)) and yaw (\(\psi\)) of the vehicle under test can be obtained directly from the operative angles \(\alpha\) and \(\beta\) imposed to the test rig in the experimental setup, using the above-presented formulas of Equation (1) with \(\beta\) where \(\beta^{\prime}\).
## 3 Results  
### Sensor Simulation of Viewing Extent  
Figure 2 illustrates the viewing extent simulation steps described in Section 2.1. Figure 2a shows the nadir points of a single sensor, e.g., Ku-band SAR in Table 1, as simulated by TAT-C tool for a 1-day period of integration. These nadir points are then extended to the swath width coverage, as shown in Figure 2b.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Tundra** & **Taiga** & **Maritime** & **Ephemeral** & **Prairie** & **Alpine** \\ \hline Radiometer & 1 & 0 & 0 & 1 & 1 & 0 \\ SAR & 1 & 0 & 1 & 1 & 1 & 1 \\ LiDAR (cloud-free) & 1 & 1 & 1 & 1 & 1 & 1 \\ LiDAR (cloud-covered) & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}  
0 = contains no skill; I = contains skill.  
\end{table}
Table 3: Assumed weight of sensor efficacy, \(W_{i,j}\), for snow mass estimation in each snow class, \(j\). Individual weights are subjective but serve as an effective skill estimate for each sensor relative to one another.  
Figure 2: Satellite viewing extent simulation of the hypothetical Ku-band SAR in Table 1 for (**a**) nadir points for the ascending pass during a 1-day integration period; and (**b**) viewing extent for the ascending pass during a 1-day integration period.  
Similarly, Figure 3 shows the sensor ground track from TAT-C and 1-day set of results of viewing extent for each of the sensors introduced in Section 2.1.  
The viewed area depends on the sensor's swath width, as well as the orbital configuration. The narrow swath sensors generally have larger gaps in coverage across space and time and, hence, longer revisit intervals relative to the wide swath sensors.
## 2 Materials and Methods  
### Data Acquisition  
#### 2.1.1 GIMMS3g NDVI Dataset  
The GIMMS3g v1 dataset ([[https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1/](https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1/)]([https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1/](https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1/)), accessed on 5 October 2022) has a resolution of 8 km and is carried on the Advanced Very High Resolution Radiometer (AVHRR) satellite sensor. To avoid adverse effects, the dataset was preprocessed with radiometric correction and coordinate transformation. This dataset is synthesised using the maximum value synthesis method for 15 days, which can  
Figure 1: Comparison of phenological parameters under five extraction methods. The figure is the result of using five extraction methods to extract the SOS and EOS of a grid point (57.5\({}^{\circ}\)\(\sim\)57.583\({}^{\circ}\)E, 60.833\({}^{\circ}\)\(\sim\)60.917\({}^{\circ}\)N) in 2000. (**A–E**) represent the five extraction methods. The black dots in the figure represent the NDVI values of 12 months in 2000, and the black lines represent the fitting results of double logistic functions, and the dotted lines represent the results of extracting the SOS and EOS by applying five extraction methods, respectively, and the details of the five extraction methods have been introduced in Section 2.3.  
effectively reduce non-vegetation interference. The GIMMS3g NDVI dataset optimises the calibration detection method for melting snow and solves the problem of GIMMS NDVI discontinuity north of 72\({}^{\circ}\)N. As the time period of this dataset is from 1982 to 2015, our research only focuses on this time range.
# A Review of Flywheel Energy Storage System Technologies and Their Applications
## 3 FESS Characteristics  
The main characteristics of flywheels are a high cycle life (hundreds of thousands), long calendar life (more than 20 years), fast response, high round trip efficiency, high charge and discharge rates, high power density, high energy density [7; 9], and low environmental impacts [2; 3; 9; 28; 29; 61]. The state of charge can be easily measured from the rotational speed and is not affected by life or temperature [9]. On the downside, flywheel self-discharge at a much higher rate than other storage mediums and flywheel rotors can be hazardous, if not designed safely.  
Flywheels have a long life time and very low operational and maintenance requirements. The cycle life is also high, compared to many other energy storage systems, as flywheels do not require long charge-discharge cycles. It can be charged and discharged rapidly, based on the application and functionality, and is not affected by the depth of discharge (DoD). The life time is estimated to be more than 20 years and a charge-discharge cycle life in excess of hundreds of thousands, with no deterioration in its performance [3; 28; 29]. The technology is capable of transferring large amounts of power in seconds, with a high roundtrip energy efficiency in the range of 90%-95% [1; 2; 18]. It can deliver its stored energy and recharge quickly, in a matter of seconds. It is an environmentally friendly technology and there are no emissions as a result of its operation, since the material used is not hazardous to the environment [18; 29]. The power and energy ratings of flywheels are independent and each can be optimized, based on the application of the energy storage. The power rating of a flywheel depends on the size of MG and associated power electronics, where its energy rating is determined by the size and speed of the rotor [3; 8].  
There are a wide range of applications of flywheels in high power output for short periods; however, in addition to the rotor material, a longer storage period requires developing new rotor designs (e.g., larger diameter rotors and/or rotor laminations), to allow longer storage durations [8]. The size of FESS is an important characteristic which puts them in competition with other energy storage technologies. Flywheels can have power densities up to five to ten times that of batteries. Due to their relatively lower volume requirements and longer working life, they can replace batteries in certain applications, including in transportation and space vehicles [8].
# Joint Channel Estimation Algorithm Based on DFT and DWT
## 6 Simulation and Analysis  
In this study, the proposed channel estimation algorithm was simulated and evaluated using MATLAB software, and the simulation parameters were set as shown in Table 2.  
In the simulation, the D model and the urban micro-room (UMi) model in the IEEE802.11 ax standard were selected as the indoor and outdoor transmission simulation scenarios, respectively [23]. Table 3 presents the key parameters of the two models.
## 2 Materials and Methods
### Health Recommender System  
The HRS leveraged data collected from patient questionnaires (examples in Appendix A Table A1) and sensory devices to identify trigger events specific to each patient, which in turn triggered personalized recommendations. The lower-level sensory data was used to generate patient-specific recommendations. Figure 1 illustrates the detailed interconnection between modules on the TeNDER ecosystem.  
The system-generated recommendations were aimed at improving various aspects of patients' daily life, such as social interactions, sleep, nutrition, environment, emotions, physical activity, and medication adherence (refer to Appendix B Table A2 for some examples). These recommendations were activated based on the static observations collected from devices worn by the patients and were selected using predefined rules. The recommendations were linked to the area of recommendation based on the following connections:  
* Social. Fall detection + social questionnaires.
* Sleep. Sleep tracker + sleep questionnaires.
* Nutritional. Localization sensor + nutrition questionnaires.
* Environmental. Environmental sensor (temperature and humidity).
* Emotional. No specific trigger.
* Physical activity. Wristband data (steps) + physical activity questionnaires.
* Other. Included medication reminders.  
The HRS played a crucial role in supporting patient health by providing tailored recommendations based on a patient's specific needs and evolution. To do this, it relied on the electronic health record (EHR) [41] microservice to access the unique claim key for a specific individual and their associated health records (observations). This interaction was facilitated through the use of a RESTful Application Programming Interface (REST API) developed, to exchange information and allowed for seamless communication between the HRS and the EHR. EHRs were intended to improve the efficiency and accuracy of healthcare by providing healthcare providers and researchers with immediate access to a patient's medical information [42].  
Regarding the methodology employed to set up the HRS, we developed it based on a user-centered approach which is outlined in the following steps:  
* Requirement gathering: We identified the needs and requirements of the target users (chronic patients). This phase was intended for the creation of the different recommendation areas.
* Design: We designed the architecture of the HRS, and the integration with other services, including the data collection (from EHR), notification system (from RabbitMQ), and user interfaces (mobile app).
* Development: We developed the HRS using Python language [43], a high-level programming language known for its versatility and functionality. Python's capabilities for data manipulation, exploration, and analysis were especially valuable in creating a system that was optimized for personalized recommendations.  
Testing and evaluation: We conducted several rounds of testing and evaluation to ensure the quality and integration of the HRS with other componentes of the project.
## 4 Discussion  
What observations are needed to study terrestrial snow across the northern hemisphere? Mission planners want to maximize the scientific value given a fixed budget. However, the exact approach to maximize snow science remains an open question. This research is designed to assist researchers and mission planners weighing the different trade-offs based on sensor selection and orbital configurations. In addition, this study explores a range of snow researchers' requirements (in terms of temporal integration periods) for remote sensing snow observations. This study also provides key information relevant to a future observing system simulation experiments (OSSE) to be conducted in a follow-up study. An OSSE serves to mimic nature and help quantitatively explore the impact of different hypothetical observing systems (such as the snow sensors explored here) on conditional (a.k.a. updated) snow model results. Furthermore, this study provides a slew of sensor coverage simulations with various sensor swath widths and orbital configurations. The results from this study will be applied in a data assimilation experiment in a similar manner as Reference [57], which was only applied to LiDAR remote sensing of snow. For researchers who are interested in similar topics, the simulated sensor viewing coverage of the hypothetical sensors in this study are published on DRUM at the University of Maryland ([[https://drum.lib.umd.edu/handle/1903/27610](https://drum.lib.umd.edu/handle/1903/27610)]([https://drum.lib.umd.edu/handle/1903/27610](https://drum.lib.umd.edu/handle/1903/27610))) (accessed on 25 December 2021) and available for public access. This study has several aspects that should be refined in the future regarding the simulation method and the metrics for the use of coverage evaluation. First, snow that is viewed does not necessarily mean that it can be retrieved accurately. There is a complex, nonlinear calculation going from the sensor observations to snow retrievals. The increase of viewing extent only represents the upper-bound on the quantity of observations, not what the retrievals will actually see. Meanwhile, the increase in the number of observations does not guarantee an improvement in snow retrieval quality. To avoid introducing more noise due to information related to something other than snow mass (e.g., snow grain size, snow wetness), efficacy weights are used to reflect the sensor skill as a function of different snow classes. Another limitation is that snow is assumed to be uniform within a given snow class. The weight matrix in Table 3 is used to describe sensor efficacy as a first-order estimate. However, sensor efficacy depends on the specific snow conditions (e.g., dry versus wet snow, shallow versus deep snow). Besides, the skill of LiDAR to retrieve snow depth is adversely impacted by clouds in a complex, nonlinear manner. When LiDAR's efficacy over cloudy regions is set to zero in Table 3, it is a first-order estimate without direct consideration of the cloud optical thickness. That is, even though cloud cover may
###### Abstract  
In the present paper, the computational fluid dynamics (CFD) numerical simulation was utilized to investigate the effectiveness of the transverse traveling wave wall (TWW) method with the expectation of inhibiting the vortex shedding from a fixed circular cylinder. We mainly focused on the variations of four kinds of wave propagation directions, five different maximum wave amplitudes and ten different wave velocities for suppressing vortices shedding and aerodynamic forces. The aerodynamic coefficients and vortex structures under different propagation directions, wave amplitudes, wave numbers and wave velocities were investigated in detail. The results demonstrate that the alternate wake behind the cylinder can be effectively eliminated resorting to the \"Downstream\" propagating TWW. The mean drag coefficient is positively associated with wave velocity. Drag and lift coefficients remain relatively stable at different wave amplitudes. When the velocity ratio (wave velocity divided by incoming velocity) is 1.5, the lift coefficient fluctuation decreases to the minimum. In contrast, the optimal combination of control parameters under the present Reynolds number is concluded with \"Downstream\" propagating direction, maximum wave amplitude ratio of 0.02, and velocity ratio of 1.5.  
flow control; traveling wave wall; circular cylinder; numerical simulation; CFD +
Footnote †: journal: Applied sciences
# The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs
## 5 Performance Evaluation
### Slide Behavior with Different Mobility Models
#### 5.2.1 Effect of Simulation Parameters
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**With GM** & **T1 = 2 s** & **T1 = 2 s** & **T1 = 8 s** & **T1 = 8 s** \\  & **v = 3 m/s** & **v = 5 m/s** & **v = 3 m/s** & **v = 5 m/s** \\ \hline \(450\times 350\times 30\) & 4.90 & 13.00 & 14.97 & 20.07 \\ \(500\times 400\times 30\) & 3.97 & 9.23 & 11.77 & 14.57 \\ \(500\times 400\times 35\) & 2.77 & 7.80 & 8.93 & 13.30 \\ \(550\times 450\times 35\) & 1.63 & 5.80 & 6.73 & 11.20 \\ \hline
**With EGM** & & & & \\ \hline \(450\times 350\times 30\) & 3.47 & 10.70 & 10.07 & 14.73 \\ \(500\times 400\times 30\) & 2.27 & 7.57 & 8.13 & 12.37 \\ \(500\times 400\times 35\) & 2.33 & 6.41 & 6.60 & 10.33 \\ \(550\times 450\times 35\) & 1.53 & 5.60 & 5.48 & 8.38 \\ \hline
**With RWP** & & & & \\ \hline \(450\times 350\times 30\) & 0.37 & 1.00 & 0.87 & 1.67 \\ \(500\times 400\times 30\) & 0.47 & 0.67 & 0.87 & 1.57 \\ \(500\times 400\times 35\) & 0.33 & 0.70 & 0.77 & 1.20 \\ \(550\times 450\times 35\) & 0.17 & 0.23 & 0.60 & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average false alarms with different airspace dimensions.
# Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
## 1 Introduction
[PERSON] and [PERSON] [24] developed a time-series approach through orbital images from the Sentinel-2 satellite to estimate sugarcane yield at the individual block level. Results showed that the maximum Green Normalized Difference Vegetation Index (GNDVI) values across the block were most correlated (r = 0.93) with the average actual yield, reported by the sugar mill. [PERSON] et al. [25] investigated RF (Random Forest) predictive models considering VIs from Landsat TM to study sugarcane yield under rainfed and irrigated conditions for two sugarcane varieties. The results suggested a strong relationship for predicting sugarcane yield, mainly, under irrigated conditions. Despite the satisfactory prediction accuracy, the field data collection was performed using manual harvesting (average yield values per plot). As such, the site-specific yield estimation is still a gap to bridge in research. Another common feature of previous research is the reliance on a single image at a given crop stage to investigate the relationship between spectral VIs and yield. Given the relatively higher temporal resolution of available imagery (for example, the revisit frequency of Sentinel-2 constellation is five days), and the potential of novel analytic tools to handle many variable predictors (see below for commentary on Machine Learning techniques), a time series approach for sugarcane yield prediction is worthwhile to investigate. Machine Learning (ML) techniques have been shown to provide higher prediction accuracy compared to the traditional statistical analyses as well as to identify dataset patterns [26; 27; 28]. These models are commonly subjected to tests and training processes, depending on the complexity of the dataset. The potential of ML techniques, such as the RF and ANN (Artificial Neural Network), for agronomical data, was verified by [PERSON] et al. [29] to estimate the LAI of soybeans. The authors concluded that RF, which is a non-parametric method, is more accurate to estimate when sample plots and variation are relatively large. [PERSON] et al. [30] and [PERSON] et al. [31] also reported the application of RF and ANN to assess the above-ground biomass of wheat and maize, respectively. These studies verified that the RF algorithm provided better results than other ML techniques. [PERSON] et al. [32] evaluated the contribution of weather variables to estimate corn yield based on RS data and RF algorithm, which resulted in a mean absolute error (MAE) of about 0.89 Mg ha\({}^{-1}\). Thus, considering the previous experiences, the purpose of this investigation was to assess spectral bands and derived-VIs from orbital images to develop predictive sugarcane yield models based on RF and MLR (Multiple Linear Regression) algorithms. The specific objectives were (i) to develop a yield prediction model based on time-series analysis of Sentinel-2 images and (ii) to compare the accuracy of MLR and RF regression methods.
## 4 Discussion
### Broader Ecological, Agricultural, and Socio-Economic Implications of Vegetation Changes in Aksu  
The observed increase in NDVI across Aksu, largely driven by urban expansion and agricultural activities, holds significant ecological, agricultural, and socio-economic implications. Ecologically, the expansion of vegetation contributes to improved soil stability and increased carbon sequestration potential, which are crucial for mitigating desertification and promoting ecological resilience in arid environments. From an agricultural perspective, enhanced vegetation cover supports the productivity of economically important crops such as cotton and jujube, reinforcing local livelihoods and contributing to regional economic stability. However, this agricultural expansion places greater pressure on water resources, posing socio-economic challenges. Given Aksu's limited water supply, the growing irrigation demands may risk depleting local aquifers and straining regional water resources [63]. Addressing these concerns calls for implementing advanced irrigation techniques and developing water management policies that balance agricultural growth with ecological sustainability.
## 3 Methodology
### Procedures for Model Construction  
#### 3.3.1 Data Selection and Division  
In modelling the various approaches presented in this study, the hold-out cross-validation technique was employed to partition the entire 101 datasets. The datasets were split into the 80:20 ratio. The first 80% of the total datasets were used as the training set (representing 81 training datasets). The remaining 20% (representing 20 datasets) were used as the test set. This strategy was adopted because [86; 87] have proved that a ratio of 80:20 or 70:30 will produce accurate prediction results and will not cause overfitting.