esa-sceva/satcom-chunk-collection huggingface dataset:
# Plant Exosomal Vesicles: Perspective Information Nanocarriers in Biomedicine
## 2 General Characteristics  
Membrane vesicles (exosomes) are biogenic nano-formations with a characteristic size of 30-200 nm, which are released from the cell by fusion of the multivesicular body with the plasma membrane [18]. Other varieties of extracellular membrane structures differ from the exosomes by their size, origin, and function. For example, the particles of 200-1000 nm, formed by budding of the plasma membrane, are related to microvesicles, while the structures of more than 1000 nm are related to apoptotic bodies, the products of the cell decay via programmed cell death [19]. The existence of nanovesicles in plants was questioned until numerous studies proving their presence in all plant organs were carried out in the last decade [20]. Morphologically, the plant nanosveicles have a rounded shape formed by a phospholipid bilayer with an average thickness of 5.3 nm [21]. The exosomal membrane protects their molecular contents from enzymatic degradation, as well as from environmental influences (e.g., high and low temperatures, extreme pH, high salinity, moisture, and sunlight) [22; 23; 24]. Any parts of plants can serve as a source of nanovesicles for biomedical purposes, with the most preferred being the leaves, fruits, and apoplastic fluid. It should be noted that the different plant organs produce different amounts of nanovesicles with unique compositions and properties, which may reflect their specializations in intercellular communications [25].
# SKVOS: Sketch-Based Video Object Segmentation with a Large-Scale Benchmark +
## 3 Sketch-Based VOS Benchmark
### Data Collection and Preprocessing  
The sketch data were collected using an interface based on the FSCOCO dataset [70]. Similar to language reference methods [14], participants were asked to sketch the target objects appearing in the first frame, without viewing the full video. Each participant was randomly assigned an object and given a blank canvas to sketch on. They had 60 s to recall as many details as possible about the object's pose, shape, and fine-grained features before the object was removed. The goal was to create a sketch that would allow someone who had not seen the video to identify the target object. To evaluate the quality of the sketches, we enlisted 20 volunteers to validate our dataset. Each volunteer was provided with a video and a corresponding sketch of an object. Initially, the first frame of the video was displayed for a while, followed by playback of the video. The volunteers were then asked to label the object in the video using a bounding box. If the automatic segmentation failed, they were asked to redraw the sketch. To maintain sketch diversity, participants were not trained and were encouraged to draw in their own style. As shown in Figure 3, the sketches varied in style, but the key visual properties (e.g., pose) of each object were consistently depicted. On average, each object was annotated with three sketches, with each annotator spending about 30 s drawing a sketch. One major challenge in video object segmentation (VOS) is the presence of similar-looking instances within a video as shown in Figure 1. Based on the approach of Sketch-a-Segmenter [56], sketches that are aligned in terms of position and scale help improve instance-level object segmentation. Therefore, all sketches in our dataset underwent this preprocessing step before experimental evaluation.  
Figure 2: Examples from our Sketch-VOS dataset, where the same target is depicted at different drawing levels: (**a**) good sketches capture the shape and details of the target more precisely, while (**b**) poor sketches roughly depict the target, reflecting the limited drawing skills of the annotators. (**c**) shows the video frames corresponding to the sketch.
## 4 Discussion
### Effect of Medium Recycling  
In the current process, 75% of the harvested medium was recycled (Figure 1). According to [PERSON] et al. [24], the cost of alkaline agent reaches 91% of the total medium cost. The current medium recycling process saved 68% of the medium cost, under the assumption that other nutrients are supplemented at every recycling. The medium cost can be further reduced if the medium recycling rate and nutrient supplementation are optimized.  
With high CO\({}_{2}\) supply and medium recycling, accumulation of DIC and DOC were observed in all Runs (Figures 2 and 3). Previous studies reported reduced growth rate with high DIC (\(>\)0.3 mol L\({}^{-1}\); [17]) and DOC (\(>\)60-100 mg L\({}^{-1}\); [8; 29]). However, in this study, no significant reduction in biomass production rate or in growth rate was observed. The DIC accumulation seemingly did not have much effect at the current level (up to 0.4 mol L\({}^{-1}\)). On the other hand, DOC concentration was 2-3 times higher than the reported inhibition level. The reason this DOC did not show clear inhibition could be the difference in DOC characteristics. A previous study with a green alga _Scenedesmus acumintatus_ reported differential inhibitive levels of DOC depending on growth stages, where DOC from the declining phase had severer inhibition than that from exponential and stationary phases [30]. Differential DOC inhibition levels were also observed between the indoor and outdoor culture of _Arthospira_, where DOC in indoor culture inhibited the growth at as low DOC as 65 mg L\({}^{-1}\), while that in outdoor culture did not significantly inhibit the growth even at 900 mg L\({}^{-1}\)[24]. Since the current semi-continuous culture maintained continuous growth, cells may not have excreted inhibitive DOC. Further study is needed to elucidate the effect of high CO\({}_{2}\) supply on the characteristics of DOC.  
Nevertheless, further DOC accumulation may cause contamination of heterotrophic microorganisms and growth inhibition, and therefore should be avoided. The previous study reports differential amounts of DOC excretion depending on culture conditions. In a culture of _A. platensis_, DOC release was the most substantial during stationary-phase (\(>\) 30% primary production), but it was below 5% during the exponential phase [31]. Excretion of DOC could be decreased through raising the culture-specific growth rate, by maintaining actively growing cells. Since the specific growth rate can be controlled by adjusting the dilution rate [32], a high dilution rate of the PBR may help to keep cells active for minimized DOC production. The excretion of DOC also changes with nutrient availability [33; 34], mechanical mixing, and so on. Further study is necessary to elucidate the effect of those conditions on the production and inhibitive characteristics of DOC.
## 3 System Model
### Energy Storage Model  
Similarly, energy storage can also be employed to compensate the breach amount of DR resource. Storage will supply energy when users are unwilling to take part in DR. Assume that \(Soc_{n}^{t}\) is energy state of storage at the beginning of time slot \(t\). Considering energy conversion losses in the operation, storage charging and discharging efficiencies are represented by \(\eta_{\text{ch}}\) and \(\eta_{\text{dis}}\), respectively. Accordingly, if energy state of storage is \(Soc_{n}^{t-1}\) at the beginning of time \(t\), then storage state \(Soc_{n}^{t}\) can be calculated as [28]  
Figure 1: Framework of demand response (DR) market with community operator considering DR resource’s breach.  
\[Soc_{n}^{t}=Soc_{n}^{t-1}+\eta_{\rm ch}e_{n}^{\rm ch,\it{t}}-1/\eta_{\rm dis}e_{n} ^{\rm dis,\it{t}} \tag{2}\]  
where \(0<\eta_{\rm ch},\eta_{\rm dis}<1\), \(e_{n}^{\rm ch,\it{t}}\) and \(e_{n}^{\rm dis,\it{t}}\) are charging and discharging energy in time slot \(t\), respectively. Since this paper mainly concentrates on the load peak shaving with DR resource, energy storage in time slots \(\mathcal{T}\) is mainly on discharging state. Therefore, the value of \(e_{n}^{\rm ch,\it{t}}\) in time slot \(t\) is 0.
# High-Precision Chip Detection Using YOLO-Based Methods
## 3 Results  
### Data Resources  
This study conducted two experimental campaigns. Chip images were systematically acquired through turning operations, with video data collected under diverse machining parameters. The first experimental dataset was captured using a Pentax KS2 digital camera  
Figure 7: Video-level post-processing algorithm.  
equipped with a Pentax DA 16-45 mm f/4 ED AL lens, configured at a 40 mm focal length. The shooting distance between the camera and the workpiece was approximately 500 mm, and 73 videos capturing machine tool cutting chips were recorded, with a total duration of about 4 h. The pixel size was 720 \(\times\) 480 for each frame image. In the turning experiments, the turning machine tool was the CA6140A horizontal lathe, and the chip image data were primarily collected from the chips produced during the outer circle turning process. The cutting depth was 1.5 mm, with a rotation speed of 260 r/min and a cutting speed of 0.16 mm/r. Figure 8 illustrates the chip targets within a real processing site environment, in line with the actual production conditions. The second experimental campaign was conducted using a HUAWEI P60 smartphone positioned at an approximate working distance of 1 m, capturing 42 video clips, totaling approximately one hour in duration. The machining setup employed a CK6140S CNC lathe with a 50-mm-diameter workpiece, maintaining the following cutting parameters: rotation speed \(V_{c}=90\) m/min, cutting speed \(f=0.12\) mm/r, and cutting depth \(ap=1.4\) mm.  
The first and second experiments yielded 73 and 42 videos, respectively. From these, 105 videos (67 from Experiment 1, 38 from Experiment 2) were processed into 28,234 chip images for training. The remaining 10 videos (six from Experiment 1, four from Experiment 2) served as test data: first, they were decomposed into 5567 images to evaluate the model's accuracy in detecting chips; second, they were tested as complete videos to assess the overall system's accuracy. All images were reshaped to 640 \(\times\) 640.
###### Abstract  
Envelope current signals are increasingly emerging in power systems, and their parameter identification is particularly necessary for accurate measurement of electrical energy. In order to analyze the envelope current signal, the harmonic parameters, as well as the envelope parameters, need to be calculated. The interpolation fast Fourier transform (FFT) is a widely used approach which can estimate the signal frequency with high precision, but it cannot calculate the envelope parameters of the signal. Therefore, this paper proposes an improved method based on windowed interpolation FFT (WIFFT) and differential evolution (DE). The amplitude and phase parameters obtained through WIFFT and the envelope parameters estimated by the envelope analysis are optimized using the DE algorithm, which makes full use of the performance advantage of DE. The simulation results show that the proposed method can improve the accuracy of the harmonic parameters and the envelope parameter significantly. In addition, it has good anti-noise ability and high precision.  
envelope signal; windowed interpolation; differential evolution; parameter identification 1 [interpolation FFT (WIFFT) is applied to overcome these two defects [18; 19]. Therefore, this paper will use WIFFT, a simple and effective method, to analyze the harmonic parameters of the envelope signal.  
In order to analyze an envelope signal, both the harmonic parameters and envelope parameters need to be calculated. WIFFT is good at harmonic parameter analysis of stationary harmonic signal [19; 20]. However, it has difficulty in handling the envelope signal under dynamic conditions, because it cannot analyze the envelope parameters. On the other hand, the envelope parameters can be obtained by an envelope analysis method (e.g., curve fitting), but the information on the harmonics is not involved in envelope analysis. It is obvious that the two processes, WIFFT and envelope analysis, are relatively independent. Therefore, this paper combines them for envelope signal analysis. Since the parameter values obtained by WIFFT and envelope analysis are not accurate enough, an appropriate optimization method should be used for further analysis. The envelope current signal is a nonlinear function of envelope parameters and harmonic parameters. Hence, parameter identification is essentially a nonlinear optimization problem. Differential Evolution (DE) is a kind of simple, fast, robust and global optimization algorithm, which has an obvious advantage in nonlinear and non-differentiable continuous space problems [21; 22]. Therefore, this paper presents an improved parameter identification method for envelope signals based on the WIFFT and DE algorithms.  
This paper is organized as follows: after some revision of the principle of WIFFT and DE algorithms, the idea and the implementation process of the combination method of WIFFT and DE are given. Then this paper analyzes the performance of this combination method through a simulation example. Finally, an envelope current signal of electric locomotive is applied to verify the simulation results.
## 4 Discussion
### Relative Airborne Pathogen Risk Transmission  
The relative airborne pathogen transmission risk analysis is based on the \(AR\) indicator according to CO\({}_{2}\) concentrations. This metric is categorized into three levels--low, medium, and high--based on prior research [12; 29]. Outbreaks are considered unlikely when the \(AR\)remains below 0.5% (the threshold between low and medium levels). However, the risk escalates to high when the AR reaches or exceeds 5%.  
Using the high-risk threshold as an outbreak criterion in classrooms, the analysis of \(AR\) reveals a significant increase in the risk of SARS-CoV-2 transmission, particularly under the second and third scenarios (speaking occupants and shouting occupants). The 5% \(AR\) high-risk threshold is reached at CO\({}_{2}\) concentrations as low as 525 ppm. In both the second and third scenarios, this threshold is exceeded even at CO\({}_{2}\) concentrations close to those found outdoors. As expected, the third scenario presents the most concerning results due to the elevated vocalization levels, with an initial \(AR\) starting at approximately 27%.  
However, the most concerning findings come from the second hypothesis, as it most accurately represents the current conditions in Classroom A. In this scenario, the \(AR\) exceeds the 5% threshold almost immediately and continues to rise, reaching over 80% at CO\({}_{2}\) concentrations of 1200 ppm. Based on the data presented in Figure 10, infants in Classroom A may spend up to 50% of their time exposed to \(AR\) levels exceeding 70%.  
It is noteworthy that only a handful of the monitored days show average CO\({}_{2}\) concentrations below the current permitted threshold. Using the already existing mechanical extraction system in the bathroom, which has a capacity of 50 L/s, could significantly improve the indoor conditions. This would reduce the relative risk indicated by the AR, bringing it down to 20% and 62% in scenarios 1 and 2, respectively.  
Moreover, these findings strongly suggest that the current regulatory CO\({}_{2}\) threshold of 770 ppm is inadequate during periods of high respiratory infection incidence. At this threshold, the \(AR\) exceeds 20% under the first scenario and approaches 70% under the second, highlighting the limitations of relying on this standard alone. Lowering the threshold to 550 ppm, as suggested by [PERSON] et al. [36] and UNE 171380:2024 [87], could significantly decrease infection risk in spaces with vulnerable, unmasked occupants. However, achieving this lower threshold would require the implementation of \(CMV\) systems capable of guaranteeing sufficient air exchange without compromising thermal comfort for both infants and caregivers.
# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
###### Abstract
It provides continuous, real-time monitoring without the inconvenience of painful skin penetration or frequent sensor replacement. Furthermore, the accuracy of PPG reflecting blood glucose variations has been improved through advanced algorithms and signal processing techniques, such as EMD, which were not applied to traditional methods. This study aims to demonstrate how PPG, particularly through a wrist-worn device, can improve patient compliance and diabetes management by providing a viable and user-friendly alternative to traditional blood glucose monitoring methods. In this study, we explore the application of wrist PPG data to estimate blood glucose levels, emphasizing only PPG signal-based features. The contributions of this study can be stated as follows:  
* The study focuses on PPG-based features for blood glucose estimation, aiming to reduce dependence on conventional features by exclusively utilizing PPG waveform-based features including ratios (AC/DCs) and ratio of ratios across various wavelength combinations, and IMF-based features derived through empirical mode decomposition (EMD). Empirical mode decomposition has been newly applied to extract PPG signal features for blood glucose level estimation. * In this study, we have performed a detailed comparative analysis, measuring the performance of PPG-based features against well-established machine learning algorithms, including XGBoost, random forest, LightGBM, and CatBoost. * Improved and more reliable blood glucose predictions were achieved using only PPG signal-based features without the need for external information such as BMI, SpO2, and age. * The proposed study focuses on wrist PPG data with wearable applications in mind, while previous results [5; 6; 9] are based on fingertip PPG data.
## 5 Conclusions  
AFS with naranjilla in the Ecuadorian Amazon constitute a sustainable alternative for its production because the yield obtained with and without fertilization exceeded the national average production by more than 50%. In terms of yield, several of the evaluated AFSs were not statistically different to the monoculture, which means that both systems are productive, but the AFS can provide intangible environmental benefits, such as minimizing the expansion of the agricultural frontier and the reduction of the use of agrochemicals.  
The patterns of the C:N ratio found in the AFS showed that the legume species used in this research increased this parameter, and thus the C sequestration improved, which is environmentally beneficial.  
In the AFS, the quantity of added biomass influenced the amount of nutrients incorporated into the naranjilla crop. Results also showed that the legume biomass had a relationship with the soil nutrients and the number of earthworms. Therefore, these events allow the suggestion that the legume species used in this study might be considered as a good source of nutrients for this fruit crop because they incorporated Mg, B, and Fe, which contributed positively to crop yield; and they were also related to soil nutrients such as Zn that benefit the presence of earthworms.  
The decrease in earthworms in monocultures shows the need to evaluate conventional methods of agricultural production in the Ecuadorian Amazon, with the aim of promoting studies that enable the restoration of soil fauna (mainly earthworms) that allow soils to be rebuilt and contribute positively to the production systems. The use of sustainable production systems should be promoted through the use of AFS in the Ecuadorian Amazon in order to restore biotic abundance and reduce the loss of biodiversity.  
Conceptualization: Y.V., A.D., L.T., and W.V.; methodology: Y.V., J.M., and A.D.; statistical analysis: M.A. and W.V.-C. writing--original draft preparation: W.V., Y.V., W.V.-C., C.C., and L.T.; writing--review and editing: W.V., Y.V., L.T., M.A., and W.V.-C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was funded by the National Institute of Agricultural Research (INIAP) and the European Union's Horizon 2020 MSCA-RISE 2019 programme under grant agreement 872384.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** We thank the agronomists of the EECA Fruit Program y de la Granja Experimental Palora and the reviewers for their comments. This project received funding from the European Union's Horizon 2020 MSCA-RISE 2019 programme under grant agreement 872384.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Preparation of Y\({}_{2}\)O\({}_{3}\):Eu\({}^{3+}\) Coated on MF or TMF  
Aggregated Y\({}_{2}\)O\({}_{3}\):Eu\({}^{3+}\) nanoparticles were synthesized by a template method. The Y\({}_{2}\)O\({}_{3}\):Eu\({}^{3+}\) was dispersed into nanosol by bead-mill process [21]. The size of primary particles was in the range of 85-130 nm, while the concentration of nanosol after milling was 2 wt%. Y\({}_{2}\)O\({}_{3}\):Eu@MF or Y\({}_{2}\)O\({}_{3}\):Eu@TMF were prepared by wet coating method as described below. First, 10 g MF or TMF were added in 100 mL distilled water or ethanol and stirred for 20 min at room temperature. The initial pH of the solution was adjusted to 6-8, by adding 1 M HCl or NaOH, and stirred for 20 min. Y\({}_{2}\)O\({}_{3}\):Eu\({}^{3+}\) (50 mL) nanosol of 5-30 wt% (based on MF or TMF weight) was injected slowly with 0.2 mL/min feeding rate into the MF or TMF's slurry along with stirring at 200 rpm. Finally, the mixture was again adjusted to pH 6-8, and then treated at 65-95 \({}^{\circ}\)C for 24 h. The resultant products were filtered and washed with distilled water and dried at 100 \({}^{\circ}\)C for 4 h. The dried powders were calcined at 600-850 \({}^{\circ}\)C for 4 h to coat tightly and enhance the luminescent property. In order to optimize the coating ability of the Y\({}_{2}\)O\({}_{3}\):Eu\({}^{3+}\) nanoparticle on the surface of MF or TMF, it was carried out by varying factors such as pH, Y\({}_{2}\)O\({}_{3}\):Eu\({}^{3+}\) coating amount, and reaction temperature, as summarized in Table 1. While the schematic diagram for the preparation of Y\({}_{2}\)O\({}_{3}\):Eu@MF or Y\({}_{2}\)O\({}_{3}\):Eu@TMF is shown in Figure 1.