esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Section
### Photoperiodic Light Treatments  
The plants were grown under 180 umol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) PPF provided by white (W) light-emitting diodes (LEDs) (MEF50120, More Electronics Co., Ltd., Changwon, Korea), under light treatment regimens of either a long day (LD, 16 h light/8 h dark), short-day (SD, 10 h light/14 h dark), or SD with a night interruption (NI) for a total of 4 h (from 11:00 p.m. to 3:00 a.m.), using LEDs at an intensity of 10 umol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) PPF. The photoperiod was started at 8:00 a.m. every day in all treatments. The light quality of the night interruption (NI) was shifted from one state to another after the first 2 h. Light quality combinations included all possible paired arrangements of blue (B, 450 nm), red (R, 660 nm), far-red (Fr, 730 nm), and white (W, 400-750 nm, with 28% B, 37% R, and 15% Fr light) light, as follows: from B to R (NI-BR), from R to B (NI-RB), from R to Fr (NI-RFr), from Fr to R (NI-FrR), from B to Fr (NI-Brr), from Fr to B (NI-FrB), from W to B (NI-WB), from B to W (NI-BW), from Fr to W (NI-FrW), from W to Fr (NI-WFr), from R to W (NI-RW), and from W to R (NI-WR) (Figure 4). All NI treatments were provided to plants separately, by subdividing the plant growth chamber with black cloth to prevent the spatial overlap of different light treatments. The LD and uninterrupted SD conditions were used as controls.  
The spectral distributions of all light treatments were scanned using a spectroradiometer (USB 2000 Fiber Optic Spectrometer, Ocean Optics Inc., Dunedin, FL, USA; wavelength detection range of 200 to 1000 nm) 25 cm from the bench top, at an interval of 1 nm (Figure 5). In each light treatment, the spectral distribution and the average value of maximum absolute irradiance were measured at three locations on the plant growing bench.  
Figure 4: Representative image of the shifts in the light quality of night interruption (NI) by light emitting diodes (LEDs) during 4 h night interruptions (NI) of the 10 h short-day (SD) treatments in _Dendranthema grandiflora_ “Gaya Yellow”: NI-BR, blue to red; NI-RB, red to blue; NI-RFr, red to far-red; NI-FrR, far-red to red; NI-BFr, blue to far-red; NI-FrB, far-red to blue; NI-WB, white to blue; NI-BW, blue to white; NI-FrW, far-red to white; NI-WFr, white to far-red; NI-RW, red to white; and NI-WR, white to red. The LD indicates the 16 h long day treatment.  
Figure 5: Spectral distribution of light used in a closed walk-in growth chamber: white light emitting diodes (LEDs) used as daily light (**A**) and different LEDs (B, blue; R, red; Fr, far-red; and W, white) used as light of night interruption (NI) (**B**).
## 2 Related Work
### Time-Series Data Generation  
Many real-world datasets are time-series, which exhibit higher dynamicity and complexity compared to i.i.d. data. Consequently, time-series generation models should address issues arising from temporal dependencies. [PERSON] et al. [27] proposed a time-series generation method based on dynamic time warping (DTW) barycenter averaging (DBA). This method generates new time-series by calculating a weighted average of the time-series within a given dataset. By assigning different weights to each time-series, a variety of rich patterns can be generated. Additionally, this study presented several weight selection methods to ensure diversity in time-series synthesis.  
Recently, deep neural network (DNN)-based models have been employed to more effectively capture the temporal dependency and dynamicity in data synthesis. Powerful generative models based on variational inference [10] and generative adversarial networks (GAN) [28] are among these approaches. [PERSON] et al. [29] proposed a GAN-based model for synthesizing power consumption data in smart grids. This study represented power consumption data using two attributes (level and pattern) and conditionally modeled the data probabilistically over users, days, and months, enabling the model to learn temporal attributes.  
[PERSON] et al. [30] presents a novel model called LS4, a deep latent state space model for time-series generation. It addresses the limitations of existing ordinary differential equations (ODE)-based models, particularly their struggles with sharp transitions in time-series data and high computational costs. LS4 leverages a convolutional representation to enhance speed and efficiency, significantly outperforming previous models in accuracy and computational efficiency, especially on datasets with irregular sampling and long sequences.
# Fall Detection Based on Recurrent Neural Networks and Accelerometer Data from Smartphones
## 3 Results
Given this, the model shows potential for deployment in real world applications, where reliability and accuracy are essential to ensure trustworthy alerts in critical situations on time. Table 2 presents the results of related solutions available in the literature (including ours). Each record represents an algorithm proposed by the authors to solve the problem. The table includes information about the paper, publication year, validation method, the algorithm used in the solution, and the accuracy score. Unfortunately, not all authors share the source codes. Thus, we are not able to reproduce all the results. Additionally, some solutions use different approaches to validate the model. For instance, [PERSON] [12] uses a simple train/test split, where the test is a 20% subset of randomly selected records. On the other hand, [PERSON] et al. [14] performs random subsampling cross-validation. Assuming the approach of the original paper [3] we can divide the results into two categories: those that report the average accuracy score of \(k\)-Fold Cross-Validation and those that use Leave-One-Subject-Out validation. The article introducing the dataset [3] achieved the highest results using the 5-Fold Cross-Validation method, achieving a 98.57% accuracy score. Another approach using this validation method was proposed by [PERSON] et al. [9]. They used RF, SVM, and DNN algorithms, of which the highest score of 96.73% was reached by DNN. [PERSON] [12], using a different method of data division and an autoencoder as a feature extractor, achieved a result of 98.17%. [PERSON] et al. [14] improved the results, splitting the data using random sub-sampling for Cross-Validation, regardless of the adopted network architecture, meaning the number of hidden layers added. The ResNet-based model by [PERSON] et al. [18] achieved the highest result for 5-Fold Cross-Validation among competitors (0.05 percentage points more than our model). On the other hand, our model dominated the competitive solutions (0.51 percentage points) for the Leave-One-Subject-Out strategy. Figure 3 presents the accuracy score for solutions that used the Leave-One-Subject-Out validation strategy. We can clearly see that our solution outperforms competitors in fall detection, achieving a 98.99% accuracy score. However, the results differ when the models are evaluated using the \(k\)-Fold Cross-Validation strategy. Figure 4 presents the accuracy scores of the algorithms under this evaluation protocol. Notably, the method proposed by [PERSON] et al. [18] achieves the highest accuracy for 5-Fold Cross-Validation, with a score that is nearly identical to ours (the difference amounts to only 0.05%). This marginal gap suggests that the potential for further improvement in terms of classification accuracy may be limited. Consequently,future research efforts might be better directed toward optimizing other aspects of fall detection models, such as inference time or computational efficiency, particularly in the context of real-time applications.
## 4 Discussion
### Reasons for Selecting Indicators and Testing for Collinearity and Correlation
#### 4.2.2 Correlation and Collinearity Diagnosis between Indicators
Additionally, the impact of factors like precipitation and temperature on ecological quality in ecological interactions exhibits spatiotemporal variability and may even lead to negative impacts [113]. The correlations between PM2.5 and precipitation or temperature may involve the interactions of regional climate systems [114]. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **GDP** & **HD** & **LAI** & **Soil** & **Tmp** & **Pre** & **PM2.5** & **NDVI** & **GPP** & **LC** \\ \hline VIF & 1.939 & 1.331 & 5.968 & 1.557 & 1.718 & 3.183 & 1.780 & 4.543 & 7.250 & 2.308 \\ Tolerance & 0.516 & 0.751 & 0.168 & 0.642 & 0.582 & 0.314 & 0.562 & 0.220 & 0.138 & 0.433 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of collinearity diagnosis for each indicator. Numbers with underline indicate insignificant results. Examining specific urban agglomerations, the BTH region with an EQI between 0.4 and 0.6 has undergone significant ecological degradation, corresponding to the expansion of human land in the southeast (Figure 3a-c). This indicates that the influence of human activities, mainly land cover changes and socio-economic factors, on ecological quality is gradually increasing [115]. Over the past 20 years, there has been a noticeable increase in the correlation coefficients of LC, GDP, and HD with EQI (Figure 10a,d). For the CYRD, human influence factors and outcomes, including PM2.5, HD, and GDP, have steadily impacted ecological quality, especially GDP (Figure 10b,e). The higher proportion of land transitions to impervious areas, concentrated around water systems and metropolitan areas, has led to a substantial decrease in EQI, as visible in Figure 3e. In the PRD region, there are more prominent human-land contradictions due to the fragmented and complex ecological landscape and highly urbanized land management [116]. It exhibits the highest correlation of EQI with HD and GDP among the three urban agglomerations (Figure 10c,f). However, the negative impact of climate factors such as PM2.5 on ecological quality has significantly reduced, indicating that the PRD region has achieved notable success in addressing ecological degradation, particularly in managing air pollution [117]. The continuous urban land expansion in plain areas and ecological protection in hilly areas have led to a clear polarization trend in the ecological quality of PRD (Figure 3i).
# A Novel IEEE 802.15.4e DSME MAC for Wireless Sensor Networks
## 5 Performance Analysis
### Throughput  
Normalized throughput is the ratio of average time occupied by the successful transmission to the interval between two consecutive transmissions. Let \(S\) be the normalized throughput of a device. The channel is sensed busy if at least one device is transmitting data. Let \(P_{tr}\) be the probability that there is at least one transmission. Since there are \(M\) devices attached to the coordinator and \(\phi\) is the probability that a device attempts its first CCA, then  
\[P_{tr}=(1-(1-\phi)^{M})(1-\alpha)(1-\beta) \tag{26}\]  
Hence the normalized throughput of a device is  
\[S=\frac{P_{tr}P_{S}L_{D}}{(1-P_{tr})\sigma+P_{tr}P_{S}T_{s}+P_{tr}(1-P_{S})T_{c}} \tag{27}\]  
where, \(T_{s}\) is the mean time of successful transmission, and \(T_{c}\) is the mean time that the channel is occupied because of the collision. \(L_{D}\) is the time of payload and \(\sigma\) is the duration of a unit backoff slot. \(T_{s}\) and \(T_{c}\) are calculated as  
\[\left\{\begin{array}{ll}T_{s}&=2T_{CCA}+2\sigma+T_{D}+\delta+T_{ACK}\\ T_{c}&=2T_{CCA}+2\sigma+T_{D}+\delta_{max}\end{array}\right\} \tag{28}\]  
where, \(T_{CCA}\), \(T_{D}\), \(T_{ACK}\), \(\delta\), \(\delta_{max}\) and \(\sigma\) are the time duration for performing \(CCA\), transmitting data, receiving an acknowledgement, waiting time for an acknowledgement and duration of one backoff slot, respectively.
## 4 Results
### Loading Effects  
In order to investigate the contribution of the aerodynamic loading on the rotor, the OPL set of tests was performed. As reported in Table 5, the corresponding change in Reynolds and Mach numbers is negligible. Results reported in Figures 13 and 14 for the lower (OPLU) and higher (OPLL) loading respectively, evidences similar features to what found when reducing the expansion ratio. For the loaded case (OPLL), where a mean rotor incidence close to OP4 is imposed, very intense secondary flows and wake appear, somehow similar to what measured for low expansion ratio in OP4. When the incidence is reduced (OPLU) a dramatic reduction in the viscous and vortex structures is detected, as found for OP1. The tip passage vortex is, again, dramatically affected by the change in the rotor incidence, almost doubling its magnitude (in terms of over/under-turning) passing from low to high loading. These results indicate that, when changing the expansion ratio, the \"indirect\" change in rotor incidence can play a major role on the rotor aerodynamics, comparable to that of the \"direct\" change in Reynolds and Mach numbers.  
Interestingly, the periodic unsteadiness also follows the same trend. Figures 15 and 16 show the periodic unsteadiness measured for the unloaded and loaded cases, respectively, and clearly show that the stator-rotor interaction strongly increases with the rotor loading. In particular, the increase of rotor incidence angle experienced by the rotor for OPLL and OP4 makes both the passage vortices strongly unsteady. This result can be explained by considering the fluctuation of rotor-inlet flow angle in combination to the very large flow turning imparted by the rotor (\(\sim\)110\({}^{\circ}\)). Due to the non-linear dependence between the passage vortex magnitude and the flow turning, a given inlet flow fluctuation does produce higher fluctuations at higher rotor loading. This is what actually occurs for both OPLL and OP4, even though in this latter case the increase in incidence is an indirect effect.  
Figure 14: Rotor-exit relative flow field in OPLL, (**a**) relative total pressure coefficient; (**b**) deviation angle.  
Figure 13: Rotor-exit relative flow field in OPLU, (**a**) relative total pressure coefficient; (**b**) deviation angle.  
Figure 15: RMS of periodic unsteadiness for OPLU, (**a**) RMS of the relative total pressure coefficient; (**b**) RMS of the deviation angle.
## 3 EMS Selection and Description
### EMSs for FCEVs: Brief State of the Art
#### 3.1.2 Optimization-Based Strategies  
Optimization-based strategies seek to achieve the best possible performance by minimizing a cost function, which typically involves fuel consumption and/or emissions.  
Dynamic Programming (DP)  
DP provides an optimal solution by considering all possible future states and decisions. However, it is computationally prohibitive for real-time applications due to the \"curse of dimensionality\" [9; 10]. DP also serves as a benchmark for evaluating the performance of other EMS approaches.  
Pontryagin's Minimum Principle (PMP)  
PMP reduces computational complexity by transforming the optimization problem into a set of differential equations. PMP is more suitable for real-time applications, but still requires significant computational resources [9]. PMP also offers a balance between optimality and computational feasibility.  
The Equivalent Consumption Minimization Strategy (ECMS) is an implementation of offline PMP for real-time applications [9; 10]. The ECMS optimizes fuel consumption in hybrid vehicles by incorporating a penalty for battery power use into the cost function. This penalty is influenced by the equivalent fuel consumption factor, which is generally dependent on SOC limits, battery current direction, and the driving cycle, allowing the ECMS to achieve near-optimal real-time results [15].  
Model Predictive Control (MPC)  
MPC uses a vehicle plant model to predict future states and optimize control actions over a finite horizon [16]. MPC is very effective, but it requires precise modeling and significant computational power.  
Optimization-based strategies can achieve near-optimal performance and are adaptable to varying conditions. However, they come with high computational demands and complexity in implementation. These strategies are more suitable for applications where computational resources are not a limiting factor and/or where achieving optimal performance is crucial.
# Improving Healthcare Facilities in Remote Areas Using Cutting-Edge Technologies
## 2 Background
### Cloud Computing Use Cases in Healthcare  
Since the COVID-19 pandemic, cloud computing in healthcare has become more prevalent. With a predicted global market size of $25.54 billion by 2024, the importance of cloud computing in the healthcare industry cannot be overstated. Undoubtedly, cloud computing is an essential tool for the healthcare business to provide a first-rate, person-centered service to its patients [30]. Among the crucial applications of cloud computing in healthcare are the following [31; 32; 33; 34; 35]:  
* Improved analysis and monitoring of data pertaining to the diagnosis and treatment of various illnesses
* Massive storage capacity for huge electronic health record (EHR) and radiological imaging files
* Capability to offer access to computer resources on-demand
* Sharing EHR with only authorized physicians, doctors, and hospitals in various parts of the globe allows rapid access to life-saving information and decreases the need for redundant testing
* Better data analysis
* Enhanced monitoring of patients' health information
## 3 Results  
### Study Population  
Medical records from 142 patients were reviewed; they consisted of 65 (45.77%) T-SCI patients and 77 (54.23%) NT-SCI patients. Demographic characteristics are presented in Table 1. The average age of the T-SCI patients was 47.95 \(\pm\) 14.67 years and for the NT-SCI patients, 51.56 \(\pm\) 13.98 years, respectively. Patients with NT-SCI were older than patients with T-SCI (64.9% of the NT-SCI group were more than 50 years). In the T-SCI group, 16 (24.6%) of the patients were females and 49 (75.4%) were males. In the NT-SCI group, 37 (48.1%) of the patients were females and 40 (51.9%) of them were males. The majority of the patients are from a rural environment, 58 (75.3%) from NT-SCI and 42 (64.6%) from T-SCI. When the patients were evaluated on the basis of occupation/profession, which we found in the T-SCI group consisted of 8 working individuals, 34 retired individuals, 1 unemployed, and 22 with disability certificates according to Romanian law, whereas for the patients in the nontraumatic group, we found 17 working individuals, 38 retired individuals, 2 unemployed, and 22 with disability certificates. When the two groups were compared, the percentage of patients who were retired represented the majority in both groups (T-SCI 52.3% and NT-SCI 49.4%). We also analyzed the medication for both groups (Table 1) and we noticed, in the T-SCI groups, most took no medication (40%), followed by antispasmodic (35.38%), and in NT-SCI group, the representative category was antihypertenisve (46.75%). Concerning the Carmeli score at admission (Table 1), we found that there were no statistically significant differences between the median values of the Carmeli Score across the categories of etiology (T-SCI/NT-SCI): \(p=0.478>\alpha=0.05\) (Independent Samples Median test), and also that the distribution of the Carmeli score was the same across the categories of etiology (T-SCI/NT-SCI): \(p=0.180>\alpha=0.05\) (Independent Samples Mann-Whitney U test).  
Cause of injury in T-SCI patients (Figure 1) consisted of 28 (43.08%) road/work accidents, 18 (27.69%) direct aggression trauma, and 19 (29.23%) falls. We measured the duration of trauma in weeks (mean 19.41, standard deviation 15.64, minimum 2, maximum 84). Regarding the cause of injury in the NT-SCI (Figure 1) patients, it consisted of syringomyelia 1 (1.30%), transverse myelitis 8 (10.39%), spondylosis with myelopathy 24 (31.17%), cervical disc disorder with myelopathy 15 (19.48%), lumbar disc disorder with myelopathy 9 (11.69%), vertebral fracture other than traumatic 10 (12.99%), medullary/vertebral carcinoma 4 (5.19%), Morb Pott-operated sequelae 2 (2.60%), sequelae operated on the dorsal arteriovenous malformation 2 (2.60%), and poliomyelitis sequelae 2 (2.60%).  
Figure 1: Causes of T-SCI (**left**) and causes of NT-SCI (**right**).
## 3 Influence of Key Parameters on the Response
### FE Model: Influence of Mesh Size in the Contact Area  
In this section the effects of the mesh size of the contact patch are assessed. Different FE models have been created varying the mapped mesh at the blade-strip contact interfaces and keeping unchanged the remaining model mesh discretization (see nominal values in Table 1). Three models were created: as shown in Figure 5a, the contact patches are modeled with different levels of accuracy. In the full stick linear analysis, the three contact springs (with stiffness \(k_{n}\), \(k_{tx}\) and \(k_{ty}\)) are inserted between each couple of blade-strip master contact nodes as shown in Figure 2. The results of the full stick linear analysis are summarized in Figure 5b.  
By increasing the mesh refinement, the system natural frequency decreases. This is to be expected since the refined mesh takes into account local flexibility and, as a result, the model becomes less stiff as the number of finite elements is increased.  
The effect of the mesh refinement is minor (lower than a +0.6% increment with a \(MMF\) = 0.25) because the compliance of the contact springs is definitely higher than that of the mesh.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Mesh Size** & **Number of Master** & **Contact** \\  & & **Contact Nodes** & **Parameter Values** \\ \hline \multirow{2}{*}{**Nominal value**} & Rectangular grid 1, & 715 nodes per & \(k_{ng}\) = 1000 N/μm \\  & [2.5 \(\times\) 1] mm elements & contact patch & \(k_{tg}\) = 233 N/μm \\ \hline \multirow{2}{*}{**Explored variation**} & _MMF_\({}^{2}\) = 1, [2.5 \(\times\) 1] mm elements & \multirow{2}{*}{2 to 715 nodes per} & 0.5 \(\leq\) _SMF_\({}^{3}\) \(\leq\) 100 for \(k_{ng}\) \\  & _MMF_ = 0.5, [1.25 \(\times\) 1] mm elements & \multirow{2}{*}{contact patch} & 0.5 \(\leq\) _SMF_\({}^{3}\) \(\leq\) 100 for \(k_{tg}\) \\  & _MMF_ = 0.25, [0.63 \(\times\) 1] mm elements & & \\ \hline \hline \end{tabular}  
* Size of contact patch: [100 \(\times\) 10] mm; 2 Mesh Multiplying Factor (\(MMF\)); 3 Stiffness Multiplying Factor (\(SMF\ast k_{ng}\)).  
\end{table}
Table 1: Definition of “nominal” key modelling choices and investigated variations.  
Figure 5: (**a**) Blade-strip contact interface with different mesh multiplying factors (_MMF_). (**b**) Frequency increment produced by mesh size variations expressed through the \(MMF\).
# COMS Visible Channel Calibration Using Moon Observation Data
## 2 Data and Methods
### Methodology
#### 2.2.1 Computation of the ROLO Irradiance
 \tag{5}\]  
where \(\mathrm{A_{\lambda}}\) is the disk-equivalent reflectance, \(\mathrm{g}\) is the absolute phase angle (radiance when used with \(\mathrm{a_{i\lambda}}\) or degree when used with \(\mathrm{p_{i}}\)), \(\theta\) and \(\varphi\) are the selenographic latitude and longitude of the sub-observer (degree), respectively, and \(\Phi\) is the selenographic longitude of the Sun (radian). To determine the disk-equivalent reflectance, observation times of the Sun, Moon, and satellite were required. Additionally, Earth-centered, Earth-fixed (ECEF) coordinates needed to be converted to Moon-centered, Moon-fixed (MCMF) coordinates [28]. In Equation (5), eight coefficients (\(\mathrm{c_{1}}\), \(\mathrm{c_{2}}\), \(\mathrm{c_{3}}\), \(\mathrm{c_{4}}\), \(\mathrm{p_{1}}\), \(\mathrm{p_{2}}\), \(\mathrm{p_{3}}\), and \(\mathrm{p_{4}}\)) are constant over all wavelengths. The other ROLO coefficients are given for the specific MI visible wavelength in Appendix B[29]. Since acquisitions were not necessarily performed at standard distances, the ROLO model was corrected to consider measurement conditions. The distance corrected irradiances (\(\mathrm{I_{ROLO}}\)) are given in Equation (6),  
\[\mathrm{I_{ROLO}}=\ \mathrm{I_{ROLO\_E}}\ \times\ \bigg{(}\frac{384,400}{ \mathrm{D_{m-v}}}\bigg{)}^{2}\bigg{(}\frac{1}{\mathrm{D_{m-s}}}\bigg{)}^{2}, \tag{6}\]  
where \(\mathrm{D_{m\text{-v}}}\) is the Moon-viewer distance in km (i.e., Moon-satellite) and \(\mathrm{D_{m\text{-s}}}\) is the Moon-Sun distance in AU.