esa-sceva/satcom-chunk-collection huggingface dataset:
## Abstract  
Green and facile approaches aiming at the manufacture of biocompatible paper-based optical sensors reporting the presence of photoluminescence (PL) modulating compounds is an emerging field of research. This study investigates the preparation of bacterial cellulose nanopaper containing covalently immobilized carbon dots for potential biosensing applications. Preliminary work of this feasibility study included TEMPO-mediated ((2,2,6,6-tetramethyl)piperidin-1-yl)oxyl-mediated) oxidation and nanofibrillation of bacterial cellulose (TOBC) on the one hand as well as synthesis and comparative analysis of different types of carbon dots (CDs) on the other hand. The two source materials of the targeted functional nanopaper were finally linked to each other by two different \(N\)-(3-dimethylaminopropyl)-\(N^{\prime}\)-ethylcarbodiimide hydrochloride/\(N\)-hydroxysuccinimide (EDC/NHS) coupling approaches to clarify whether grafting of CDs prior to or after TOBC paper formation would be the method of choice. Synthesis of the carbon nanodots was accomplished by microwave-assisted co-hydrothermolysis of appropriate precursor compounds. After isolation and purification by dialysis particles in the single-digit nanometer-range were obtained and characterized with regard to their photoluminescence properties in terms of emission wavelength, pH stability, and quantum yield. All types of synthesized CDs reached their PL maxima (450-480 nm; light blue) in a narrow excitation wavelength range of 340-360 nm. Variation of molar (C/N) ratio of the CD precursors and substitution of the nitrogen donor EDEA by urea increased PL and quantum yield (QY), respectively. The highest relative QY of nearly 32% was obtained for CDs synthesized from citric acid and urea. PL of all CDs was virtually insensitive to pH changes in the range of 4-10. Tensile testing of hybrid nanopaper prepared after EDC/NHS-mediated grafting of GEA-type CDs onto TOBC (0.52 mmol:g-1 COOH) in dispersion state revealed that both stiffness and strength are not compromised by incorporation of carbon dots, while plastic deformation and elongation at break increased slightly compared to nanopaper formed prior to decoration with CDs. Water contact angle of the nanopaper is unaffected by introduction of carbon dots which is supposedly due to the presence of surface amino- and amide groups compensating for the loss of carboxyl groups by grafting.  
bacterial cellulose; hybrid nanopaper; TEMPO oxidation; carbon dots; microwave-assisted thermolysis; EDC/NHS coupling; photoluminescence; sensing applications
## 3 Results
### Serum Biochemistry Parameters
#### 3.5.2 Proteins and Enzymes  
Figure 7 depicts the effects of different treatment conditions on specific protein and enzyme levels in WKY and SHRs receiving TW, NAB, TW/NAB, AB and TW/AB. Regarding albumin (Figure 7A), the analysis of serum levels shows a significant difference (**_p_ < 0.01) between the TW/NAB and TW/AB groups in WKY, whereas no differences were found in SHRs. Rats in the TW/AB group exhibit higher albumin levels compared to those in the TW/NAB group. The ALT activity (Figure 7B) reveals significant intergroup differences. The TW/AB group shows a significant elevation (*_p_ < 0.05) in ALT levels compared to the TW, TW/NAB and AB groups in WKY. Borderline non-significant increases appears in SHRs after the intake of TW/NAB or TW/AB. In the case of AST (Figure 7C), there is a trend towards increased enzyme activity in the WKY receiving TW/AB and in SHRs  
Figure 6: Serum electrolyte levels in Wistar–Kyoto rats (WKY) and spontaneously hypertensive rats (SHR): (**A**) chloride (Cl\({}^{-}\)), (**B**) sodium (Na\({}^{+}\)), and (**C**) potassium (K\({}^{+}\)). Experimental groups included animals provided with tap water (TW), alcohol-free beer (NAB), tap water or alcohol-free beer at their discretion (TW/NAB), alcoholic beer (AB), or tap water and alcoholic beer at their discretion (TW/AB). Results are expressed in milliequivalents per liter (mEq/L) as mean \(\pm\) SEM (n = 8; *_p_ < 0.05; **_p_ < 0.01).  
receiving TW/NAB, but without significant differences. There are no significant differences observed in the ALP levels among the treatment groups in either WKY or SHRs (Figure 7D).
## 1 Introduction  
Currently, we face challenges such as global warming and overconsumption of natural resources, and it is essential to continue seeking solutions on Earth. However, it is equally important to explore alternatives for resources beyond our planet to ensure the human survival. Currently, we face another issue: space explorations today are limited by the data communication capabilities of devices in space, primarily due to the lack of a space communications infrastructure.  
This paper presents an innovative solution by proposing a network infrastructure consisting of strategically positioned nodes orbiting celestial bodies to address the lack of space communication infrastructure. These nodes work cooperatively to maintain efficient and stable communications for coverage of celestial bodies within the asteroid belt, addressing the proposed features of a unified communication architecture [1; 2; 3].  
The paper is organized as follows. Section 2 describes the proposed communication network infrastructure, with hardware descriptions and their placement on celestial bodies. Section 3 outlines the network architecture, proposing routing protocols for different hierarchy levels within the network. Finally, the conclusions are presented in Section 4.
## 3 Experimental Application
### Challenges and Limitations  
There were several challenges encountered during the experiment, and each provided valuable insights for future improvements. Some of the challenges and limitations are described below:  
1. Mechanical limitations: The high yield strength of copper and niobium materials, combined with the large size of the cavity structure, makes them resistant to deformation, requiring a large amount of force for adjustment. This makes manual tuning difficult and prevents simultaneous adjustments in multiple orientations, which can easily affect coaxiality. This highlighted the need for high-strength and robust mechanical components in the pre-tuning machine in order to handle the high-stresses during adjustment.  
2. Motor control: The stepper motors exhibited issues with lost steps while operating at higher speeds, which necessitated lower operating speeds (below 2000 steps per second) to ensure accuracy. This needs to be considered when optimizing the motor control algorithm to achieve precise adjustments.  
3. Sensor calibration: The displacement and pressure sensors required careful calibration to ensure accurate measurements. Any misalignment or calibration error could lead to inaccurate tuning results, underscoring the need for high-precision sensors in the system.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 5 Case Study: Economic Benefits of 5G-Enabled Healthcare Solutions  
Healthcare spending takes a big chunk out of the national budgets of various countries globally (e.g., healthcare spending accounts for roughly 18% of North America's and 10% of the global economy's gross domestic product (GDP) [52]). A study by the Economist Intelligence Unit on 60 countries shows the growth of healthcare expenditure in different regions (see Figure 7). It is expected that the percentage of GDP spent will rise globally mainly due to population ageing and the rise of chronic disease.  
Figure 7: Region-wise comparison of healthcare spending (as a percentage of of GDP) in 2017 and projected expenditure in 2020. (Adapted from: [52; 53]).  
In a report by Deloitte, it is estimated that 50% of global healthcare expenditures (about $4 trillion) will be spent on three leading non-communicable diseases (i.e., cardiovascular diseases, cancer and respiratory diseases) by 2020 [52]. They also projected that healthcare spending as a percentage of GDP will rise more quickly in low-income countries due to the rise in cardiovascular disease and limited government reimbursements for respiratory conditions. An analysis performed by the World Health Organization in 23 low-and middle-income countries reported the economic losses from three non-communicable diseases (stroke, heart disease and diabetes) from 2006-2016 [10]. The National Diabetes Inpatient Audit reported that at any time, around 15% of hospital beds are taken up by people with diabetes in the U.K. [54]. A major cause for the rise of these diseases is a dramatic increase in average life expectancy. Countries with the lowest relative need have the highest proportion of healthcare workers. Figure 8 presents the distribution of healthcare workforce, where the horizontal axis represents the number of health workers, the vertical axis shows the burden of disease and the size of the dots is the total health expenditure. Figure 8 clearly shows that the region of the Americas has only 10% of the global burden of disease, but almost 37% of the world's healthcare workers reside in this region; and their health expenditure is over 50% of the world's financial resources. In contrast, Africa suffers more than 24% of the global burden of disease, but has access to only 3% of health workers with less than 1% of the world's financial resources.  
Technology-enabled healthcare systems can deliver care outside the traditional setting by keeping patients at remote locations, promoting adherence to medications, lowering readmission rates and reducing cost. They not only boost healthy lifestyle, but also benefit regional and global economic growth in various ways. They offer huge commercial chances and opportunities. These opportunities are not only limited to hospitals, healthcare providers, health insurers and the pharmaceutical industry, but will become very crucial for network operators, telecommunication providers and software developers. Some of the economic benefits of technology-enabled healthcare are discussed below.
## 5 Model Implementation  
For model implementation, OpenMDAO, an open-source MDO framework provided by NASA's Glenn Research Center, was employed [38]. OpenMDAO is based on Python language and it provides useful libraries and features that include various solvers and drivers for system analysis or the multidisciplinary optimal design problem. This makes it relatively easy to perform system analysis and optimization problems, even if users are not familiar with mathematical methods.  
Figure 14 shows an expanded design structure matrix (XDSM), which summarizes the performance calculation process of an electric propulsion system, and has automatically generated using the OpenMDAO visualization feature plug-in [39; 40]. Here, \"1:indeps\" denotes a group of independent variables that correspond to the design variables in the design problem of the electric propulsion system, such as propeller diameter, pitch, and motor specifications. Similarly, \"2:consts\" denotes a group of components that are treated as constants, such as air density, power consumption in the flight control system, and payload. Groups 3 to 6 are submodels that contain the relevant constants and parameter calculations of each component of the electric propulsion system, and finally, \"7:RotorOutput\" calculates the various conditions and results of rotor operation.  
Figure 15 is a \(N^{2}\) diagram that depicts the configuration of the entire model in more detail. The same with the XDSM, the \(N^{2}\) diagram was also automatically written through the visualization feature of OpenMDAO. From the list of groups and components name on the left, the N2 diagram provides a more detailed view of the group configuration that was simply illustrated in the XDSM. For the electromotive system model implemented in this paper, all of the components, except independent variables or constants, are declared to be explicit components. Explicit components include a formula for that variable, and the various models that are presented in Section 4 are written here. Additionally, as shown in the lower triangular matrix on the \(N^{2}\) diagram, this problem includes cycles for calculations of the propeller thrust and torque, the armature reaction of the motor, and the voltage drop of the battery. Solvers applied for the problem were DirectSolver and Block Gauss-Seidel NonlinearSolver with Aiteken Relaxation [41].  
Figure 14: Extended design structure matrix for the performance calculation process of the proposed electric propulsion system model.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
###### Abstract  
The accurate identification of look-alike medical vials is essential for patient safety, particularly when similar vials contain different substances, volumes, or concentrations. Traditional methods, such as manual selection or barcode-based identification, are prone to human error or face reliability issues under varying lighting conditions. This study addresses these challenges by introducing a real-time deep learning-based vial identification system, leveraging a Lightweight YOLOv4 model optimized for edge devices. The system is integrated into a Mixed Reality (MR) environment, enabling the real-time detection and annotation of vials with immediate operator feedback. Compared to standard barcode-based methods and the baseline YOLOv4-Tiny model, the proposed approach improves identification accuracy while maintaining low computational overhead. The experimental evaluations demonstrate a mean average precision (mAP) of 98.76 percent, with an inference speed of 68 milliseconds per frame on HoloLens 2, achieving real-time performance. The results highlight the model's robustness in diverse lighting conditions and its ability to mitigate misclassifications of visually similar vials. By combining deep learning with MR, this system offers a more reliable and efficient alternative for pharmaceutical and medical applications, paving the way for AI-driven MR-assisted workflows in critical healthcare environments.  
Keywords:look-alike vial; real time detection; AI; deep learning; AI in healthcare; object detection; mixed reality; HoloLens +
Footnote †: journal:
## 2 Materials and Methods
### Data Processing
#### 2.4.8 Statistical Analysis  
The biophysical and land surface variables derived from UAV hyperspectral and thermal sensing were summarized for each plot to assess their relative changes in biochar amendment vs. control plots. The sunlit soil, shaded soil, and rice canopy pixels were identified using an unsupervised classification [51]. A k-means clustering method was used in the classification. After testing with different numbers of classes and bands, we found that using 5 clusters and 3 bands (Band 12, 29, and 104, for the false composite image in Figure 2) can effectively separate the aforementioned three types of pixels. Sunlit and shaded leaves were not separable probably due to their mixed spectral signatures and the foliage transparency (Supplementary Figure S1).  
Sunlit soil pixels at plot corridors were extracted for determining bare soil albedo and surface temperature and comparing their differences between the biochar groups the control group. Rice canopy pixels were extracted for analyzing the biochar effects on root zone soil moisture, canopy biophysical variables, and canopy surface energy components. To reduce edge effects [52], the pixels within 15 cm to borders of each plot, roughly corresponding to the edge rows of rice plants, were discarded. The valid pixels extracted from each plot were aggregated to three sub-plots (Figure 1) for further statistical analysis, to avoid spatial autocorrelation from fine resolution.  
We analyzed four days' repeated UAV results over three treatment groups with triplicate using repeated measures analysis of variance (ANOVA) [53] by the free statistic software JASP (available at [[https://jasp-stats.org/](https://jasp-stats.org/)]([https://jasp-stats.org/](https://jasp-stats.org/)), accessd on 8 April 2021). The Greenhouse-Geisser correction was used if sphericity had been violated in the repeated measures. The marginal mean and standard error of each variable were calculated for each group of three plots and four days considering the degrees of freedom within- and between-subjects. The mean variable differences between the treatment groups and the control group were analyzed using [PERSON]'s honest significant difference test [54]. For the one-time measurement of leaf and soil samples, one-way ANOVA was used to analyze if their mean values were different. The four days' repeated UAV results were compared with the in-situ measured soil water content and soil matric potential using violin plots and descriptive plots with mean and 95% confidence interval. A simple Pearson correlation was used to test the relationships between key rice growth variables and soil water availability.
## 3 Numerical Simulation
### Modeling Descriptions  
Figure 8 clearly shows the mesh division and boundary conditions for the numerical model established using FLAC\({}^{\text{3D}}\). The model includes roof overburden, coal seam, floor aquiclude and the confined aquifer, and has the geometric dimensions of 200 m (X) \(\times\) 300 m (Y) \(\times\) 240 m (Z), with a total of 33,000 elements. The bottom boundary of the numerical model is fully fixed. An equivalent vertical load of 8.25 MPa is uniformly applied on the top boundary to represent the crustal stress resulting from overburdened strata, and the other boundaries are constrained by horizontal displacement. An initial hydraulic pressure of 2.5 MPa is set in the confined aquifer to simulate the effect of groundwater on  
Figure 7: Water inrush zoning on the key strata (top view of the models in Figure 6b). (**a**) A case with horizontal key strata; (**b**) A case with inclined key strata.  
the floor strata during coal extraction. With regard to the boundary effect, the coal seam is incrementally excavated from Y = 50 m to Y = 250 m, at 10 m per step, totaling 20 steps. All failure calculations are performed according to the Mohr-Coulomb criterion. The values of the physico-mechanical parameters of rocks and the coal seam in the numerical models, as listed in Table 1, are empirically chosen and estimated according to the field test results. Figure 9 shows the geolithological division of the coal and rock strata, in which the thicknesses of the different stratum are rounded to integer values to simplify calculations.  
Figure 8: Mesh division and boundary conditions of numerical model established using FLAC3D (taking an inclined strata model as an example). (**a**) mesh division; (**b**) boundary conditions.  
Figure 9: The geolithological division of the coal and rock strata.
## 2 Materials and Methods
### Control Group  
The control group participated in a matched-duration training program designed to mimic the structure and contact time of the experimental intervention while excluding the specific asymmetry-targeted components. Training was conducted three times per week over 4 weeks (12 sessions total), with each session lasting approximately 40 min under coach supervision.  
To ensure comparability in testing conditions and participant engagement, the CG underwent the following:  
* General glyometric and mobility drills. Bilateral squat jumps, skipping drills, and mobility circuits were included, matched in total volume and intensity to the EG sessions.
* Unilateral jumps without targeted feedback. Athletes performed unilateral hops with visual display of generic movement metrics (jump count), but no real-time asymmetry or EMG data. This sham feedback condition was implemented to control for potential placebo effects associated with visual feedback.
* Stabilization and core training. Core stability circuits (planks, single-leg balance) and general conditioning tasks were applied to simulate the neuromuscular engagement of EG sessions, without intentional asymmetry correction.
* Apparent EMG monitoring. Surface electrodes were placed, and EMG signals were recorded passively without display. Athletes were informed of \"technical calibration\" procedures to partially blind them regarding the true function of the intervention.
* Progression matched by time, not performance. Unlike the EG, task difficulty was increased according to a fixed weekly schedule (e.g., repetitions or time), not based on individual asymmetry or RSI data.  
This approach ensured that both groups received equal contact time, coach interaction, and exposure to testing equipment, while maintaining experimental integrity. Although both groups were matched for session duration and overall weekly structure, physiological load parameters (e.g., heart rate, EMG amplitude) were not objectively monitored in the control group. This limitation should be considered when interpreting potential differences in neuromuscular stimulus and adaptation. Participants were not informed about group allocation or the true purpose of the biofeedback to minimize expectancy bias. Assessors conducting post-intervention testing were blinded to group assignment.
## 3 Collision Risk Avoidance Algorithm  
### Judment of Driving Behavior by PBS  
The flowchart of this system activation is shown in Figure 7. When the ego vehicle heads to an intersection, the following triggers activate the system: the driver intends to make a right turn at the intersection with the turning indicator, the on-board sensor detects an occluding vehicle on the oncoming lane, and there is a driving corridor behind the occluding vehicle where an object can dart out. In order to safely avoid a collision when darting out occurs, the vehicle velocity should be controlled in advance before the darting out occurs so that the vehicle does not enter the right-turn dilemma zone. To make this possible, when the system is activated, it calculates \(V_{safe}\) and \(V_{esc}\) at the point where the ego vehicle has traveled at the current velocity for the prediction time of \(T_{p}\) seconds by predicting darting-out. If the calculation results in \(V_{esc}>V_{safe}\), a dilemma zone exists. Therefore, PBS decelerates the vehicle to prevent it exceeding \(V_{safe}\). As the vehicle traveling straight has priority over the vehicle turning right at intersections, the ego vehicle basically travels at \(V_{safe}\) or under until the visibility improves. Additionally, if \(V_{safe}=0\), the system will stop the vehicle. The same deceleration assistance is also provided when the vehicle velocity is lower than \(V_{esc}\).  
Figure 7: Flowchart of the system activation.  
Conversely, if \(V_{safe}\geq V_{esc}\) and the vehicle velocity is higher than \(V_{esc}\), there is no dilemma zone and the ego vehicle can escape the intersection while maintaining the current velocity.