esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Cationic Surfactant (CTAB)-Water Mixture on the Heat Transfer Performance  
Figure 18 shows the steady-state heat transfer coefficients for spray cooling at different CTAB concentrations. It can be seen from Figure 18 that the surface heat transfer coefficient increases and then decreases with increasing CTAB concentration. The maximum heat transfer coefficient of 1.9 W/cm\({}^{2}\cdot\)K is reached at the concentration of 200 ppm, which is 29.3% higher than the heat transfer coefficient of 1.47 W/cm\({}^{2}\cdot\)K of pure water. Beyond this point, the concentration continued to increase, and the heat transfer coefficient decreased continuously, and when the concentration reaches 300 ppm, the heat transfer deteriorated.  
Figure 17: The influence of the coverage rate of the nozzle height change under different heat fluxes on the steady-state surface heat transfer coefficient.  
Figure 18: The steady-state heat transfer coefficients of spray cooling at different CTAB concentrations.  
The phenomenon can be analyzed in conjunction with the visualization images of the chamber interior in Figure 19. The addition of surfactant reduces the surface tension and contact angle. The higher the concentration, the more the surface tension and contact angle are reduced. The reduction in surface tension makes it easier for the droplets to overcome the surface tension and break into smaller droplets. The increased droplet number is more conducive to driving the spreading of the liquid film on the heated surface. The decrease in contact angle increases the contact area between the droplets and the surface, increasing the evaporation rate of the liquid. The increase in droplet number density leads to an increase in the number of bubbles carried by the spray. The reduction in surface tension and contact angle at lower additive concentrations plays a dominant role in heat transfer. As a result, the heat transfer coefficient increases substantially.  
After the concentration reaches the optimum value, the high foamability of the surfactant gradually dominate as the concentration continues to increase. As shown in Figure 19, the foaming inside the cavity gradually increases and hinders the flow of the surface liquid film. Therefore, the heat transfer coefficient decreases and at a concentration of 300 ppm a deterioration of heat transfer is produced. In addition, when the bubbles are detached from the liquid film, the hydrophilic groups of CTAB are adsorbed on the inner and outer surfaces of the bubbles, respectively, which increases the stability of the bubble structure. When the solution concentration is high, the molecules are closely arranged on the inner and outer surfaces of the bubble, as shown in Figure 20. The bubbles are so difficult to collapse that the heat transfer process is affected.  
Figure 19: Visualization images of the foam inside the chamber at different CTAB concentrations: (**a**) 50 ppm; (**b**) 100 ppm; (**c**) 150 ppm; (**d**) 200 ppm; (**e**) 250 ppm; (**f**) 300 ppm.
## 2 Materials and Methods
### Imaging Protocol  
CMR was performed at the Ljubljana University Medical Center using a 3 Tesla (T) scanner (Siemens TrioTim scanner, Siemens Healthineers, Forcheim, Germany). Before imaging, 0.1 mmol/kg of Gadovist, a gadolinium-based contrast agent, was administered intravenously to patients. Phase-sensitive inversion recovery imaging (PSIR) [9; 13] was used to provide good contrast between normal myocardial tissue and myocarditis-affected tissue while also providing fast imaging. Images were T1-weighted (T1w), with typical imaging parameters of repetition time (TR) 700-800 ms, echo time (TE) 1.5-1.6 ms, delay time (TD) 500-650 ms, matrix size 256 \(\times\) 256, flip angle (FA) 20\({}^{\circ}\), and in-plane resolution 1.4 \(\times\) 1.4 mm\({}^{2}\). The inversion time (TI) was optimized to null the normal myocardium signal, with its typical 250 to 350 ms values. For each patient, images were acquired on 10 to 12 SAx planes so that the LV was covered by slices from the base to apex. The typical slice thickness was 8 mm, with a spacing of 8 to 10 mm between slices.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Sample (\(n=5\))** \\ \hline Male/female & 5/0 \\ \hline Age (mean \(\pm\) standard error) (y) & \(30\pm 5\) \\ \hline BMI (mean \(\pm\) standard error) (kg/m\({}^{2}\)) & \(29\pm 2\) \\ \hline Mean LGE extent (mean \(\pm\) standard error) (\%) & \(24\pm 5\) \\ \hline Maximum troponin (mean \(\pm\) standard error) (ng/L) & 18,564 \(\pm\) 5000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the study population.
## 3 Results  
### LULC Change  
The LULC classification accuracy, as assessed on the images from 1980, 1990, 2000, 2010, and 2020, exhibited a range between 0.77 and 0.91. The corresponding Kappa coefficients were between 0.68 and 0.85 (Table 1). These results indicate a substantial degree of concurrence between the classified LULC data and the ground truth, affirming the accuracy and reliability of the classification process. The Kappa coefficients reflect a notable level of concordance, further validating the quality of the LULC classification across the different temporal layers.  
The Landsat images corresponding to the five different dates were subjected to classification, resulting in the categorization of the land cover into five distinct classes. The maps showed that over the 40-year study period, the rangelands remained the dominant land use, slightly increasing from 42.4 to 44.1% (Figure 2). The cropland area has risen from 31.3 to 35.7%, paralleled by a gradual decrease in the forest areas, from 25.2 to 18.7% (Figure 2).  
The alterations that have been noted can be attributed to a combination of moderate deforestation and a series of forest fires that impacted the Siliana region, specifically the Kisra and Makther mountains, during the period from 2010 to 2020.
## 3 Results and Discussion: Experimental Tests
### Mechanical Performance  
The mechanical properties of the nine mixes are detailed in Figure 3. It is of note that the values of the SCC containing limestone aggregate powders were adequate for structural applications, despite the use of 100% coarse RA [49].  
All changes in the SCC composition affected the mechanical behavior of the mixes:  
* On the one hand, the addition of fine RA led to a decrease in all mechanical properties, with an average reduction of 30-40% when using 100% fine RA. This decrease was almost linear for the properties dependent on bending-tensile behavior (splitting tensile strength and flexural strength, Figure 3c,d). On the contrary, the decrease in compressive strength and the modulus of elasticity for a 50% fine RA content was less than expected, considering the values obtained for 100% fine RA (Figure 3a,b). It showed the mainly negative effects of small amounts of fine RA on the bending-tensile behavior of SCC, which was already worse than that of vibrated concrete, due to its higher fines content [51]. Increased porosity and weakening of the interfacial transition zones caused by the fine RA explained this worsening of the mechanical performance [22; 27].
* On the other hand, limestone powder provided the best results for strength-related properties and especially in those properties dependent on bending-tensile behavior. As found in other studies, this aggregate powder contributed to a compact and high  
Figure 3: Mechanical performance of SCC mixes: (**a**) compressive strength; (**b**) modulus of elasticity; (**c**) splitting tensile strength; (**d**) flexural strength.  
quality cementitious matrix [48]. The use of RA powder yielded the worst mechanical results and in the same way as fine RA, its presence explained a similar yet more pronounced strength behavior in the concrete [50; 51]. Thus, a compressive strength of only 15.4 MPa was obtained when combining 100% fine RA and RA powder.
## 2 Materials and Methods
### In Vitro Activity
#### 2.5.2 Cell Viability Assay  
Mouse brain microvascular endothelial cells (BEnd.3) were cultured in DMEM medium containing 10% FBS and maintained at 37 \({}^{\circ}\)C in a humidified atmosphere with 5% CO\({}_{2}\). Drug solutions were prepared by diluting in PBS to the desired concentrations. Cells were seeded at a density of 4000 cells per well in 96-well plates. Once the cells had adhered, 25 \(\upmu\)L of LA-1 or loganin solutions at varying concentrations were added to each well, followed by a 24 h incubation. BEnd.3 cells were treated with 3.12 or 6.25 \(\upmu\)M of LA-1 or loganin for 24 h, followed by exposure to 200 \(\upmu\)M tertiary-butyl hydroperoxide (TBHP) for 4 h to induce oxidative stress. After the treatments, 25 \(\upmu\)M of MTT solution (5 mg/mL) was added to each well, and the cells were incubated for an additional 4 h. The supernatant was then carefully removed, and the formazan crystals were dissolved in 150 \(\upmu\)L of DMSO. Plates were shaken at room temperature for 15 min, and absorbance was measured at 490/570 nm using a microplate reader (Molecular Devices, SpectraMax ABS, San Jose, CA, USA).  
Cell viability = (Absorbance (drug well))/(Absorbance (control well)) \(\times\) 100%
###### Abstract  
While floating wind lidars provide reliable and cost-effective measurements, these measurements may be inaccurate due to the motion of the installation platforms. Prior studies have not distinguished between systematic errors associated with lidars and errors resulting from motion. This study will fill this gap by examining the impact of platform motion on two types of profiling wind lidar systems: the pulsed WindCube V1 (Leosphere) and the continuous-wave ZephIR 300 (Natural Power). On a moving hexapod platform, both systems were subjected to 50 controlled sinusoidal motion cases in different degrees of freedom. Two reference lidars were placed at a distance of five meters from the platform as reference lidars. Motion-induced errors in mean wind speed and turbulence intensity estimation by lidars are analyzed. Additionally, the effectiveness of a motion correction approach in reducing these errors across various scenarios is evaluated. The results indicate that presence of rotational motion leads to higher turbulence intensity (TI) estimation by moving lidars. The absolute percentage error between lidars is the highest when lidars are exposed to yaw and heave motion and is the lowest when exposed to surge motion. The correlation between lidars, though it is the lowest in the presence of pitch, yaw, and heave motion. Furthermore, applying motion compensation can compensate the correlation drop and erroneous TI estimation.  
floating lidar system; ship-based lidar; motion-induced error; motion compensation; motion correction 202416391. [[https://doi.org/10.3390/rs1671391](https://doi.org/10.3390/rs1671391)]([https://doi.org/10.3390/rs1671391](https://doi.org/10.3390/rs1671391))
## 3 Experiments and Analysis
### Comparison of Fusion Criteria
#### 3.2.1 Low Frequency Fusion  
The low frequency sub-band image inherits a large amount of energy information from the source image, so Data 1 is applied to analyze the influence of different fusion criteria on information extraction. Selection criteria include the following: (1) Mean; (2) Local_STD; (3) PCNN; (4) SML; (5) EOL Filtering; (6) SMLF. The parameter descriptions of low frequency fusion criteria are shown in Table 2, and the experimental results are shown in Figure 7.  
Figure 7a illustrates that the low frequency sub-band image processed by the Mean criterion is relatively blurred. The criterion of Mean smoothens the whole low frequency image and is not able to reflect the streamline profile of the aircraft wreck. Although the sub-band images (Figure 7b-d) maintain the advantages of high contrast and clear brightness, there is a pseudo-contour effect of  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Criterion** & \multicolumn{1}{c}{**Parameter Description**} \\ \hline Mean & Average process \\ Local\_STD & Maximum value of local standard deviation \\ PCNN & \(T_{\beta}\) soft limiting function. \(\beta_{\beta}\) average gradient. \(S_{\beta}\)\(N\)SML \\ SML & Maximum of SML value \\ EOL Filtering & Guidance map: EOL. Edge detection: GF. Source consistency: CV. \\ SMLF & Guidance map: RLV. Edge detection: GF. Source consistency: CV. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter description of low frequency fusion criteria. NSML, novel sum-modified Laplacian; SMLF, SML energy filtering; PCNN, pulse coupled neural network; EOL, energy of Laplacian; RLV, relating sum-modified Laplacian energy with visual contrast value; GF, guided filter; CV, consistency verification.  
Figure 7: Low frequency sub-band images with different fusion criteria in Data 1. (**a**) Mean. (**b**) Local_STD. (**c**) PCNN. (**d**) SML. (**e**) Energy of Laplacian (EOL) filtering. (**f**) SMLF.  
block flow around the edges of the aircraft wreckage, especially, the sub-band image processed by the criterion of Local_STD leaves the most, followed by the result of the PCNN criterion. This is the reason that PCNN model is not adopted to select the sub-band coefficients of low frequency in the early processing of subsequent experiments, so as to avoid the interference of the analysis. Figure 7e shows that the fused image processed by the criterion of EOL filtering possesses distinct brightness on the whole aircraft (nose, wings, tail), but insufficient brightness exists in the shadow of the aircraft wreckage. This fusion criterion results in a reduced visual contrast of low frequency sub-band image. Figure 7f displays that the low frequency sub-band image processed by the SMLF energy criterion has a clear contour, relatively high contrast; the brightness is roughly the same as the source image; and no false fusion pseudo-contour effect is generated. In general, the visual effect of the sub-band image processed by the proposed low frequency fusion criterion is superior to other fusion strategies.
## 3 Case Study
### TEP Dataset
#### 3.1.2 Experimental Setup  
The N-way K-shot experimental protocol was adopted as a standardized approach for few-shot fault diagnosis. For the construction of a classification task, N categories were initially selected at random. Within each category, K examples were randomly drawn to form the support set, and Q examples to construct the query set. This sampling strategy was iterated to yield 10,000 training tasks and 100 testing tasks, ensuring broad validation of the GOPML algorithm. The learning process entailed a two-tier structure: the task-level inner layer and the meta-level outer layer, with learning rates for inner updates (\(\alpha\)) and outer learning (\(\beta\)) set at 0.01 and 0.001, respectively. Five inner update steps were designated for initial learning, followed by 10 steps for fine-tuning. This extensive task generation aimed to cover the TEP dataset's diverse operating conditions and fault scenarios, enhancing the algorithm's capability to generalize across varied learning contexts.  
The base network for the TEP dataset was structured as a sequential model consisting of various layers designed to capture the temporal dependencies and features relevant for fault diagnosis, as delineated in Table 2. The parameter selection for this dataset, particularly regarding the Conv1D layer, was driven by the need to accurately model the dataset's specific fault types and operational conditions. The smaller filter size in Conv1D was optimally suited for the TEP dataset's requirements, where it is crucial to capture finer details of temporal sequences indicative of specific fault types. This precise parameter tuning enabled the GOPML algorithm to detect and classify a wide range of fault scenarios, thereby increasing the reliability and accuracy of fault diagnosis in the TEP dataset's context. The network comprised five layers, forming an integral module of the architecture. Layer 1 (L1) featured a one-dimensional convolutional layer with 64 filters of size 3, processing the input data. This was succeeded by Layer 2 (L2), applying the rectified linear unit (ReLU) activation function for non-linearity. Subsequent layers performed batch normalization (BN) and max pooling operations, while the final layer integrated a long short-term memory (LSTM) network with 100 units, essential for capturing temporal sequence information in time-series data. The base learner's structure for each module configuration within the network was uniformly designated as 'Module 1', with the last layer being a fully connected (FC) dense layer for the output layer, facilitating fault classification tasks, as presented in Table 3.
# Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS
## 2 Materials and Methods
### Data and Preprocessing  
#### 2.2.1 Remote Sensing Data and Pre-Processing  
The leaves of the study area were fully expanded around July and August when the leaf area index was considered to have basically reached its maximum value and remained basically constant over a certain period of time. The remote sensing data used for the LAI inversion and estimation of the maximum LAI came from the Sentinel-2 satellite image taken on 21 August 2019, with image level L2A, good image quality and basically cloud-free, and the remote sensing image map of the study area was obtained after clipping, as shown in Figure 2a. At the same time, the Sentinel-2 satellite image on 25 August 2020 was acquired to verify the leaf area estimation model, and the image preprocessing was consistent with that in 2019, as shown in Figure 2b. When extracting VIs from the images, the average value of the pixel at the coordinate center and its surrounding eight pixels was used as the VI of the sample plot.  
Figure 1: Location diagram of study area.  
Figure 2: Sentinel-2 true color image of study area. **(a)** 21 August 2019; **(b)** 25 August 2020.  
The MCD15A3H product (orbit number h26v04) for the study area in 2019, including LAI and photosynthetically active radiation, was obtained via the NASA website with a spatial resolution of 500 m and a temporal resolution of 4 days, for a total of 92-time phases of LAI images per year. The MODIS LAI images were pre-processed with image cropping and projection conversion to construct the time-series LAI data for 92 time phases per year in the study area. The LAI time-series curves were extracted from the MODIS LAI raster dataset. In this study, the time-series LAI data were smoothed using the locally adjusted cubic-spline capping (LACC) algorithm proposed by [PERSON] et al. [69]. The smoothed MODIS LAI dataset was used as the base dataset for further study.
# Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network
## 1 Introduction
However, it is difficult for the complex cooperation relation to be implemented for wireless sensor networks due to the limited energy and processing capability of sensor nodes. Therefore, efficient multi-node coded cooperation schemes with low complexity should be elaborately designed for wireless sensor networks to reduce the energy consumption and improve the lifetime. In this paper, multi-source sparse cooperation schemes with efficient distributed LDPC codes are proposed to transmit data from multiple sources to the mobile sink in WSN. We investigate two cooperation models. In the first model, each source node usually has one partner to help itself and it also assists another partner, where the moving sink traverses one time along the distribution region of the active source nodes. In the second model, each source node is helped by two partners and also participates in the data transmission of two cooperative partners, where the moving sink traverses twice in the region of source nodes. We call the two cooperation models as multi-source sparse cooperation due to the simple cooperation relation especially when a lot of source nodes participate in the cooperation. Specifically, the sparse cooperation models are firstly formed considering the geographical locations of the nodes, the quality of the inter-node wireless channels, and the moving trajectories of the mobile sink node. Here, the neighbors of source nodes can be chosen to serve as the cooperative partners. Then, directed graphs and cooperative matrices are involved to present the cooperation models. Finally, distributed LDPC codes based on the sparse cooperation are devised to jointly transmit the data from multiple sources to the mobile sink node. In this cooperative data collection schemes, the energy consumption of the sensor nodes can be greatly reduced and each source node has very low complexity. If more source nodes participate in the sparse cooperation, the performance can be further improved. This paper is organized as follows. Section 2 introduces two multi-source cooperation models according the moving trajectories of the mobile sink. In Section 3, the cooperation models are firstly presented by directed graphs and cooperative matrices, and then distributed quasi-cyclic LDPC codes are constructed to match the sparse cooperation models. In Section 4, the performance of the proposed schemes are evaluated by simulations. Finally, some conclusions are drawn in Section 5.
# The Use of Radio and Telemedicine by TMAS Centers in
## 4 Discussion
### What Needs to Be Done?
#### 4.1.2 Satellite Telecommunications  
Medical assistance at sea has been remarkably improved by satellite communication. Communication channels can be established between vessels and land-based medical professionals, allowing for real-time consultations, diagnosis, and intervention on-site [37; 38]. Satellite networks can transmit large amounts of data, including high-definition images and videos, which are essential for accurate diagnosis and medical support [39]. Satellite connections also allow remote monitoring of vital signs and other health parameters, making it possible for medical professionals to track the health status of patients in real-time.  
Telemedicine consultations in case of medical issues on board are becoming increasingly popular for providing health care at sea. Using this approach, doctors can provide advice and guidance to seaferers from their locations using video conferencing. This method of healthcare delivery is especially helpful for remote vessels that lack onboard medical facilities and personnel [40]. In emergencies, telemedicine consultations can help diagnose and treat illnesses and injuries in real-time, reducing the need for medical evacuation. As technology continues to improve, telemedicine will become more accessible and affordable for mariners, providing them with much-needed medical assistance at sea. Despite these benefits, there are still challenges to overcome, such as connectivity issues and regulatory hurdles. There is also a need for greater diffusion of telemedical devices on ships and specific training of ship medical officers in telemedicine.