esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
The existing approaches share three common issues when considering their application to PIG systems, including the following: (1) excessive processing complexity for real-time and resource-constrained systems, (2) late detection leaving insufficient time for corrective maneuvers, and (3) incomplete estimation of parameters. In this paper, we address these issues by proposing the RT-ETDE framework using a 3D ToF camera that consists of a set of lightweight schemes for ETDE. Unlike [14; 15], RT-ETDE does not use the whole point cloud for detection and estimation but intelligently partitions the point cloud into groups and extracts only simple representative features, such as centroid, extreme point, or the number of points, to apply simple geometric solutions. Additionally, we propose several real-time adaptation techniques to ensure the real-time constraint. This is the first work that supports early detection and full parameter estimation of elbows and T-junctions in real time.
## 4 Discussion  
EOs and some of their components exert antitumor activities against numerous cancer models, such as colon [28], lung and liver [29], by affecting multiple pathways [30; 31; 32; 33]. [PERSON] et al. [34] summarized studies showing the properties of EOs to induce in vitro and in vivo cell death in melanoma models. They also indicated the use of EOs in clinical trials with the reduction of the side effects in cancer patients. Moreover, frankinense, pine needle and geranium EOs are also able to suppress tumor progression through the regulation of the AMPK/mTOR pathway in breast cancer [35]. Several EOs also have chemopre properties [34]. In eukaryotic cells, EOs can cause depolarization of mitochondrial membranes and decrease their fluidity. In this way, EOs are able to induce severe damage in the mitochondria, leading to cell death. [PERSON] et al. demonstrated that carvacrol, a phenolic monoterpenoid, is able to cause cell death as apoptosis. This process is associated with the production of free radicals that cause the rapid consumption of the intracellular pool of antioxidants [36]. Another study by [PERSON] et al. evaluated the mechanism of action of carvacrol in the MDA-MB-231 cell line. This monoterpenoid induced cell death via cytochrome C release after mitochondria permeabilization [37]. Moreover, we also demonstrated the antimuoral activity of Tea Tree Oil (TTO), with an apoptotic effect on melanoma cell lines [38]. In general, the cytotoxic effects of EOs in toto, or those of some of their components, such as monoterpenoids [39], are due to their lipophilicity, which allows them to cross membranes and destroy them [40; 41].  
Herein, the damage caused by _V. macrophylla_ EO to the cell membranes of SKBR3 cells can be attributed to its main component, citral, which is a mixture of the two monoterpenes, neral and geranial. Citral is generally recognized as safe (GRAS) by the FDA, and is commonly used as a flavoring agent in the EU. On the other hand, this compound is endowed with antimicrotubule [42] and chemopreverities [43]. Several studies have shown that citral is able to cause cell death to tumor cells and severely damage cytoskeletal structures. On the other hand, other components occurring in the _V. macrophylla_ EO, including both major and minor constituents, may be involved in the overall synergistic effect normally displayed by the EO [26]. Moreover, as demonstrated by other authors [44], our results show that _V. macrophylla_ EO significantly changes the cytoskeletal organization of SKBR3 cancer cells, suppressing actin cytoskeletal rearrangement and destroying the cell membrane thanks in part to its monoterenoid activities. Furthermore, this EO significantly decreased the proliferation and migration of human SKBR3 cells in a concentration and time-dependent manner.  
Figure 10: SKBR3 treated with _V.pergillus macrophylla_ EO at 24 h with 1.25 \(\upmu\)g/mL (**a**) fluorescence microscopy and (**b**) scanning electron microscopy micrographs. Arrowheads indicate probably vesicles filled with EO.
## 7 Experiment Verification
### Experiment  
Concurrently, actual temperatures were collected and recorded. The results are depicted in Figure 22, showing a maximum temperature error of 1.05 \({}^{\circ}\)C, a minimum error of 0.30 \({}^{\circ}\)C, and a standard deviation of 0.284 across all 10 sets of data. These 10 sets of data include different main circuit flow rates, cooling power, working pressure, and ORFR. The CAM model maintains satisfactory predictive accuracy across various working conditions. The experimental data suggest that the CAM model accurately predicted the steady-state temperatures of the HCS under ten different randomly selected operating conditions. This demonstrates that the CAM model offers a rapid, efficient, and precise method for solving temperature prediction issues.  
To validate the accuracy of the minimum ORFR (Figure 18), two operating conditions were chosen for experimental testing. For condition 1, the load torque and ORFR were 320 N\(\cdot\)m and 15.3 L/min, respectively, while for condition 2, they were 280 N\(\cdot\)m and 11.5 L/min. The main system flow rate and cooling power remained constant at 48 L/min and 6.1 kW for both conditions. The experimental results are depicted in Figure 23.  
Figure 21: Experiment platform for HCS.  
Figure 22: Comparison between the temperature calculated by CAM and the actual temperature.  
For the two operating conditions, the hydraulic oil temperature stabilized after 7200 s. The final steady-state temperatures for operating conditions 1 and 2 are 79.5 \({}^{\circ}\)C and 79.2 \({}^{\circ}\)C, respectively, both of which remain below the oil temperature setpoint of 80.0 \({}^{\circ}\)C. This suggests that the minimum replenishment volume calculated in the paper is accurate, and the data derived from the CAM model can effectively guide engineering practice.
## 3 Nonlinear Dynamic Analysis of the RBRS
### Influence of Friction Coefficient on Dynamic Characteristics of RBRS under Variable Speed
Figure 24: The bifurcation diagram of RBRS at \(\mu=0.3\). Figure 25: 3-D frequency spectrum of RBRS at \(\mu=0.3\). In order to better display the influence of friction coefficient \(\mu\) on the dynamic characteristics of the system, Figures 28-30 show that the time domain map, frequency domain map, axis orbit map, and Poincare map of the coupling system with \(\mu=0.78\), when \(\omega=29.8\) rad/s, \(\omega=43.2\) rad/s and \(\omega=52.2\) rad/s, respectively. It can be seen from the Figures 28-30 that the vibration response of the three cases are nonlinear; in the frequency domain,\(f_{\mathrm{n}}\), \(2f_{\mathrm{n}}\), and \(3f_{\mathrm{n}}\) are the main frequency components, and there are frequency multiplication and frequency demultiplication components whose amplitude is not obvious. In addition, the axis orbit map changes from chaotic closed curves to multiple non-coincident closed curves, and then to five closed ones; and the attractors on the corresponding Poincare map  
Figure 27: 3-D frequency spectrum of RBRS at \(\mu=0.78\). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Excitation** & **Rotating Speed** & **Main Frequency Feature (m: Positive Integer)** \\
**Current \(\mu\)** & \(\omega\) **(rad/s)** & \\ \hline \multirow{4}{*}{\(\mu=0.05\)} & \(7.0<\omega\leq 15.4\) & \(f_{\mathrm{n}}\) \\  & \(15.4<\omega\leq 60.4\) & \(f_{\mathrm{n}}\), \(2f_{\mathrm{n}}\), \(3f_{\mathrm{n}}\), \(4f_{\mathrm{n}}\), \(5f_{\mathrm{n}}\), \(\ldots\)... (where: \(\omega=51.2\) (\(f_{\mathrm{n}}\), \(2f_{\mathrm{n}}\), \(3f_{\mathrm{n}}\), \(4f_{\mathrm{n}}\), \(\ldots\), continuous frequency components); \(\omega=52.6\) (\(f_{\mathrm{n}}\), \(2f_{\mathrm{n}}\), \(3f_{\mathrm{n}}\), \(4f_{\mathrm{n}}\), \(\ldots\), \(mf_{\mathrm{n}}\)/3)) \\  & \(60.4<\omega\leq 70.6\) & \(f_{\mathrm{n}}\), \(2f_{\mathrm{n}}\), \(3f_{\mathrm{n}}\), \(4f_{\mathrm{n}}\), \(\ldots\) ; \(mf_{\mathrm{n}}\)/2, \(show irregular scatters, isolated multiple points, and isolated five points, respectively. Then the system completes the alternative evolution of chaotic motion, periodic-n motion, and period-5 motion. Based on the above numerical simulation, it can be seen that the friction coefficient \(\mu\) is an important parameter affecting the dynamic characteristics of the coupling system (RBRS). With the increase of the friction coefficient, the motion state of the system becomes more complex, the chaotic region increases first and then decreases, the complex motion and periodic motion alternate frequently, and the response amplitude increases gradually.
## 3 Capacity Optimization Model
### Evaluation Mechanism  
#### 3.2.1 Economic Evaluation  
Theannual total cost (\(ATC\)) includes the initial investment cost, interaction cost with power grid, and fuel cost. The smaller \(ATC\) is, the better the economy achieves.  
\[ATC=\sum_{i=1}^{K}C_{i}N_{i}+\sum_{t=1}^{T}\left[\begin{array}{c}E_{grid,in}( t)C_{e}(t)-E_{grid,out}(t)C_{e}(t)+\\ F_{mt}(t)C_{f}(t)+F_{gb}(t)C_{f}(t)\end{array}\right] \tag{12}\]  
where \(C_{i}\) is the cost of the \(i\)-th equipment; \(N_{i}\) is the installation capacity of the \(i\)-th equipment; \(E_{grid,in}(t)\) is the amount of electricity purchased from the grid during system operation; \(E_{grid,out}(t)\) is the amount of electricity sold to the grid during the system operation [25]; \(C_{e}(t)\) is the time-of-use electricity price; \(C_{f}(t)\) is the time-sharing gas price.
# Monitoring and Data Analysis in Small-Scale Landslide Physical Model
## 4 Results
### Terrestrial Laser Scanner (TLS) and Structure-from-Motion Photogrammetry  
The scan of the small-scale landslide physical model consists of approximately 5-6 individual scans made from different positions around the model, each scan with up to 7 million points and approximate scan duration of 5:30 min. After the first scan which was made in color, scans for other two tests were monochromatic as they proved to provide better visualization of geometry due to the use of different shades of grey. Distance of the scanner from the object was from minimal 0.8 m to maximal 2 m. Initial processing and registration was performed in the FARO SCENE software. Merging of scans into a single point cloud and reduction of unnecessary number of points by a minimum distance of 1 mm performed in CloudCompare software [67]. The most important settings of the laser scanner used for the recording of the tests are stated below in Table 10.  
The resulting 3D point clouds before and after the sliding in Test 3 are presented in Figure 13a,b, respectively. Figure 13c is a photograph taken of the physical model at the end of the test, and in Figure 13d, cross-sections of the central longitudinal profile along the slope can be observed. The green dotted line presents the profile before the sliding, and the blue full line presents the profile after the sliding. Here, we can observe the movement of the sliding mass, which started at the foot of the slope, developed retrogressively to the top, traveled, and then settled on the lower horizontal part of the slope model.  
\begin{table}
\begin{tabular}{c c} \hline
**Scanner** & **FARO Focus 3D X 130** \\ \hline Resolution & 1/10 (on a scale of 1/32 to 1/1) \\ Quality & 4\(\times\) (min. 2\(\times\), max. 6\(\times\)) \\ Scan size & 7 million points \\ Point distance & 15.34 mm/10 m \\ Scan duration & 5:27 min \\ Color & No \\ Distance from the model & Min. 0.8 m to max. 2 m \\ Number of scans & 5-6 \\ \hline \end{tabular}
\end{table}
Table 10: Terrestrial laser scanner settings used in the tests.  
Figure 13: Results of terrestrial laser scanner (TLS) survey at slope inclination of 40\({}^{\circ}\): (**a**) before the test starting; (**b**) at the end of the test; (**c**) photo of the physical model at the end of the test; (**d**) contour profiles of terrain before and after the sliding—green line initial stage, blue line final stage.  
Although in the SfM survey georeferencing was not done, the results of the laser scanner and the photogrammetry are pretty similar. Instead, the point cloud obtained by the SfM photogrammetry was aligned and scaled with the use of the terrestrial laser scans which served as a reference point. The initial stage of the physical model was photographed with approximately 50 photographs and the final stage by approximately 90 photographs. The method could also give us \(x\), \(y\), and \(z\) coordinates of the object, but it definitely needs predefined static reference points to gain more precision.
###### Abstract  
The present study reports an eco-friendly synthesis method of silver nanoparticles (AgNPs) using two different extracts (aqueous and ethanolic) of _Tagetes__eecta_ flowers. When exposed to different biocompounds found in the plant, silver ions are reduced, thus, resulting in the green synthesis of nanoparticles. After performing the optimization of synthesis, the obtained AgNPs were characterized using various techniques. The UV-Vis spectrum of the synthesized nanoparticles showed maximum peaks at 410 and 420 nm. TEM analysis revealed that the particles were spherical with a size ranging from 10 to 15 nm, and EDX analysis confirmed the presence of silver metal. The average diameter value obtained through DLS analysis for the two types of AgNPs (obtained using aqueous and ethanolic extracts) was 104 and 123 nm. The Zeta potentials of the samples were \(-27.74\) mV and \(-26.46\) mV, respectively, which indicates the stability of the colloidal solution. The antioxidant and antimicrobial activities assays showed that nanoparticles obtained using the aqueous extract presented enhanced antioxidant activity compared to the corresponding extract, with both types of AgNPs exhibiting improved antifungal properties compared to the initial extracts.  
green synthesis; silver nanoparticles; marigold flowers; antioxidant; antifungal activity +
Footnote †: journal:  
Acticle  
Eco-Friendly Synthesis and Comparative In Vitro Biological Evaluation of Silver Nanoparticles Using _Tagetes__eecta_ Flower Extracts. _Appl. Sci._**2022**, _12_, 887. [[https://doi.org/10.3390/appl2020887](https://doi.org/10.3390/appl2020887)]([https://doi.org/10.3390/appl2020887](https://doi.org/10.3390/appl2020887))
## 3 System Concept
### RF Concept and Design
#### 3.1.2 Antenna Design  
Since the architecture of the radar system is bistatic, two separate antennas are required for Tx and Rx, respectively. As indicated in Figure 3, both antennas have to focus on the same spot on the human body to maximize the receive power and hence the signal-to-noise ratio (SNR). Since the antennas are placed next to each other, their beams have to be tilted towards the common focal point. In this case, the required inclination angle of the antenna beam is \(\pm 10^{\circ}\) for Tx and Rx antenna, respectively, at a target distance of 40 cm. Further requirements on the antennas are a feasibly high gain of about 19 dBi and a symmetric radiation diagram with respect to E and H plane. To ease the integration into a compact housing, a planar design is preferred.  
The developed antenna prototype is a two dimensional planar patch array on a Rogers RO4350B RF-substrate and consists of 72 radiating patches, which are arranged in nine columns of eight elements each. The size of the elements in each column is tapered with a Taylor window to optimize the radiation pattern. The feed network connecting the columns makes use of T-junctions as power splitters and introduces the required phase shift of 30\({}^{\circ}\) between the columns by curvy delay lines of different amplitudes. The chosen topology with serial delay lines leads to a very compact design, but at the same time sums up the errors of subsequent delays, which requires accurate 3D field simulations and optimizations of the entire structure. Another benefit of the feed network is its power distribution, delivering less power to the array columns the further away they are from the central element. This inherent power tapering helps to reduce the side lobe levels by slightly increasing the main lobe width.  
As shown in Figure 5, the fabricated antenna showed the desired tilt of the beam of 10\({}^{\circ}\) and a gain of 17.7 dBi with the side lobes being at least 14 dB below the main lobe level.
###### Abstract  
CANYVAL-C is a formation-flying mission that demonstrates a coronagraph utilizing two CubeSats. The coronagraph is a space telescope that blocks sunlight to examine the overcast regions around the sun. It is composed of optical and occult segments. Two spacecraft were aligned with respect to an inertial system to configure a virtual telescope using inertial alignment hold technology. The relative orbit control scenario for this mission involves rendezvous, differential air drag control, and inertial alignment holding. Orbit control algorithms and simple strategies that can be automatically constructed onboard have also been developed. For each maneuver, the control performance under the errors from navigation, attitude determination and control, and propulsion systems were assessed via Monte Carlo simulation, taking into account the hardware specifications and operations. In addition to the algorithm and strategy of this mission, the relative orbit control scenario was evaluated for its practicability using Monte Carlo simulations. The feasibility of this mission is ensured by a statistical analysis of the prospect of its success during its operation.  
Relative Orbit Control  
Astronomical and Control Laboratory, Department of Astronomy, Yonsei University,  
Seoul 03722, Republic of Korea; [EMAIL_ADDRESS]  
2 NARA Space, Busan 49111, Republic of Korea; south@[EMAIL_ADDRESS]  
2024 11 135 11  
GOMX-4 aims to demonstrate the formation-flying and inter-satellite link (ISL) technologies to prove the capabilities for future constellations [8; 9]. The CubeSat proximity operation demonstration (CPOD) validates proximity operation technologies that consist of rendezvous, proximity operations, and docking (RPOD) with miniaturized technologies [10; 11]. The objective of the Small-scale Magnetosphere and Ionosphere Plasma Experiment (SNIPE) mission is to explore the small-scale structure of terrestrial physical phenomena by collecting scientific temporal and spatial data [12; 13; 14].
## 3 Results
### Results of the Ng-Rc Based Classification  
The results of the first scenario, which focused on the application of NG-RC alone to the classification process, are presented in Table 3. The NG-RC, employed with the MLP-based classifier, achieved the highest AUC scores of 90.33% and 74.79% for intra- and inter-patient cases, respectively. An increase in the AUC score of 5.78% and 0.79% are observed for intra- and inter-patient cases when the NVAR transform was used with MLP readout. Similarly, an enhancement of 4.18% and 2.31% are registered for intra- and inter-patient cases when the NVAR transform was used with ridge readout. Conversely, the ridge readout produced the lowest score when only using the original data. This indicates that the arrhythmia classes are not linearly separable in the feature space, which requires nonlinear schemes. The obtained results of the NVAR+MLP are illustrated in the flowing confusion matrix, Figure 11.  
Figure 10: Classification performance as a function of the first hidden layer neuron’s number. Experiments were performed to select the optimal number of the first hidden layer of MLP readout evaluated on three activation functions: Tanh, ReLU, and Logistic.
## 2 Data and Methods
### Methods
#### 2.3.3 Evaluation Metrics  
The SMAP retrievals are often subject to large sampling intervals (e.g., 2-3 days or longer) in the central TP. Therefore, the evaluation of SM2 RAIN estimated precipitation was conducted on a coarser time interval basis (5 days in this study). In detail, accumulated precipitation between two adjacent retrievals (interval of N days) was first calculated using Equation (4) and then evenly assigned to each day to form a daily estimation, which was subsequently reaggregated into a 5-day accumulation. It is noteworthy that (1) SM2 RAIN estimates were not conducted when the time interval between two soil moisture records exceeded four days; (2) The daily PML-V2 evapotranspiration data were first interpolated to hourly data (by averaging daily data through simple arithmetic averaging), and then aggregated to obtain the corresponding evapotranspiration between two adjacent soil moisture records at different orbits. Finally, the SM2 RAIN estimated 5-day accumulated precipitation time series was further compared with rain gauge observations to produce six metrics (Table 2), which measure the performance of the SM2 RAIN algorithm with certain soil moisture input.  
Specifically, the Pearson correlation coefficient (\(R\)) reflecting the degree of consistency between estimated and observed precipitation, mean bias error (_BIAS_, in mm/5 d) and root mean square error (_RMSE_, in mm/5 d) characterizing the deviation magnitude of estimated precipitation compared with the observed, were served as statistical metrics. Whereas the probability of detection (_POD_) indicating the fraction of precipitation events which are correctly identified by the SM2 RAIN estimates, false alarm ratio (_FAR_) representing the  
Figure 5: (**a**) Barplot of number of effective retrievals and (**b**) boxplot of sampling intervals for SMAP ascending (SMAP_A) and descending (SMAP_D) soil moisture products during the JJAS 2015–2018.  
fraction of precipitation events which are identified by SM2 RAIN estimates but non-events observed by rain gauge, and threat score (_TS_) that combines _POD_ and _FAR_, were served as categorical metrics. These metrics, in addition to \(R\), _BIAS_, and _RMSE_, were used to appraise the capability of SM2 RAIN-derived products in capturing rainfall events. Note that during the evaluation, a threshold value of 0.5 mm/5 d, according to the classification of precipitation in climatology, was used to determine whether there was a precipitation event [64]. Clearly, higher \(R\), lower absolute _BIASs_, lower _RMSE_, higher _POD_, lower _FAR_, and higher _TS_ indicate better estimation of spatiotemporal variation of precipitation and better detectability of rainfall events.  
Furthermore, the magnitude of _RMSE_ was considered (i.e., _RMSE_ obtained with regard to default soil moisture input minus the _RMSE_ obtained with regard to improved soil moisture input) in order to measure the improvement in precipitation estimates upon the use of better satellite soil moisture quality (e.g., data with reduced retrieval uncertainty, smaller sampling interval, and both).