esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The most reliable LUS findings with COVID-19 were the presence of B-lines or determining whether a scan is normal. We did not observe statistically significant differences between the longitudinal and transverse scans. The IRR in LUS of COVID-19 patients may benefit from more standardized clinical protocols.  
Conceptualization, J.L.H., J.C. and Y.T.-C.; Methodology, Y.T.-C.; Software, J.L.H., C.F., J.C., M.M. and R.G.; Validation, M.M., R.G., R.A.-R., J.A.-T., L.M.B.-R., M.B.-W., R.B., A.C.-C., R.C.-L., J.C.-M., J.C.-R., S.G.-R., G.G.-d.-C., A.G.-R., C.H.-C., C.H.-Q., R.L.-F., D.L., R.M.-B., M.C.M.-D., M.M.-G., M.M.-B., R.N., M.N., B.O.d.U.-A., A.A.O.-G., J.M.P., S.R., D.A.R.-S., T.S., I.M.S.-B., M.T.-A. and Y.T.-C.; Formal analysis, J.L.H., C.F. and M.M.; Investigating, R.A.-R., J.-A.T., L.M.B.-R., M.B.-W., R.A.-C., R.C.-L., J.C.-M., J.C.-M., J.C.-R., S.G.-R., G.-G.-C., A.G.-C., A.H.-P., C.H.-Q., R.L.-F., D.L., R.M.-B., M.C.M.-D., M.M.-G., M.M.-B., F.M.-A., R.N., M.D., B.O.d.U.-A., A.A.O.-G., J.M.P., S.R., D.A.R.-S., T.S., I.M.S.-B., M.T.-A., J.T.-M., T.V., T.V.-J., J.J.Z.-S. and Y.T.-C.; Resources, J.C., R.G. and Y.T.-C.; Data curation, J.L.H. and C.F.; Writing--original draft, J.L.H. and C.F.; Writing--review & editing, J.L.H., C.F., J.C., M.M., R.G., R.A.-R., J.A.-T., L.M.B.-R., M.B.-W., R.B., A.C.-C., R.C.-L., J.C.-M., J.C.-R., S.G.-R., G.G.-d.-C., A.G.-R., C.H.-C., A.H.-P., C.H.-Q., R.L.-F., D.L., R.M.-B., M.C.M.-D., M.M.-G., M.M.-B., F.M.-A., R.N., M.N., B.O.d.U.-A., A.A.O.-G., J.M.P., S.R., D.A.R.-S., T.S., I.M.S.-B., M.T.-A., J.T.-M., A.T.V., T.V.-V., J.J.Z.-S. and Y.T.-C.; Visualization, A.H.-P., F.M.-A., J.J.Z.-S. and Y.T.-C.; Supervision, J.L.H., C.F., J.C., J.T.-M., A.T.V., T.V.-V. and Y.T.-C.; Funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by CDTI (Spanish acronym: Centre for Industrial Technological Development), funding number COI-20201153. Partially supported by the Google Cloud Research Credits program with the funding number GCP19980904, by the project RTI2018-099118-A-I00 founded by MCIU/AEI/FEDER UE and by the European Commission-NextGenerationEU, through CSIC's Global Health Platform (PTI Salud Global).  
The study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the IRB of Hospital Universitario Puerta de Hierro (protocol code 2.0 5 April 2021 and date of approval April 2021).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Not applicable.  
Conflicts of Interest: The authors declare no conflict of interest.
# Scaling of FAPAR from the Field to the Satellite
## 5 Conclusions  
To address the spatial and temporal mismatches between _in-situ_ measurements and satellite observations, this study proposed a general methodology for scaling FAPAR from the field to the satellite. The mismatches were addressed in three steps including temporal normalization, interpolation and spatial aggregation. Main conclusions were: (i) The method of temporal normalization was feasible and reliable. By integrating the atmospheric radiative transfer model (6S) and the FAPAR-P model, the effects of multiple influential factors (e.g., VIS, SZA and LAI) on instantaneous FAPAR were characterized accurately. The fitting on the model simulations made the final correction only dependent on the existed FAPAR measurement, SZA and LAI, which is easy to perform. The accuracy of the temporal normalization method was validated using a set of continuous _in-situ_ measurement from one day as 0.013 (RMSE); (ii) The logistic model for temporal interpolation was reliable and accurate. It maintained higher goodness and robustness of fit on _in-situ_ data than either linear or cubic model did. It captured the smooth and consistent temporal shape even with very limited ground measurements during the rapid growth stage. The RMSE and \(R^{2}\) of the final linear fit between the temporally corrected _in-situ_ data and ASTER NDVI were 0.077 and 0.934 respectively; (iii) By comparing the MODIS and upscaled ASTER FAPAR temporally and spatially, the MODIS FAPAR manifested a good agreement to the aggregated FAPAR with \(R^{2}\) of 0.922 and the RMSE of 0.054 in the study area. The performances of MODIS FAPAR product evaluated in the study is comparable to previous validation attempts, which have evaluated the MODIS C5 FAPAR product as maintaining an accuracy of about 0.1 [5; 15; 24; 29; 60; 61]. A similar validation over croplands by [24; 29] found the accuracy of MODIS FAPAR product was about 0.07-0.08, which are very close to this study (0.054).  
This work was supported by the National Basic Research Program of China (Grant No. 2013 CB733402), the key program of NSFC (Grant No. 41071224, 41371410, 41331171, and 41571341) and the China Scholarship Council. We thank [PERSON] and [PERSON] for the correction of English language and the anonymous reviewers for their constructive comments, which have helped to improve the manuscript.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL
### Schemes Forwarding Packets via a Single Parent
#### 4.1.6 Ohca  
[PERSON] et al. proposed OHCA (optimization-based hybrid congestion alleviation) which combines a resource control strategy with a traffic control strategy [20]. If a parent node detects the arrival rate exceeding the service rate, it deems the congestion condition to be met and sends its children nodes a DIO message containing congestion information. On receiving a DIO message, a child node applies the resource control strategy to select a noncongested parent. If a noncongested parent node is not found, the traffic control strategy is applied.  
For resource control, OHCA models parent selection as a multicriteria optimization problem and solves it by using a multiattribute decision making (MADM) technique called grey relational analysis (GRA). In OHCA, the objective function uses three routing metrics, buffer occupancy, ETX, and queuing delay. For each parent candidate, the GRA procedure calculates the rank considering different characteristics of the routing metrics. The main advantage of GRA is simplicity, which is crucial in resource-constrained networks.  
If the resource control strategy fails (i.e., a noncongested parent node is not found), a less congested parent is selected from the candidate parents, and then the traffic control strategy is applied based on the node priorities, application priorities, and the congestion information received from the selected parent. In OHCA, a node's sending rate adaptation is modeled as a constrained optimization problem which is solved by using the NUM (network utility maximization).  
Performance was evaluated via Cooja simulation using CSMA/ContikiMAC(RDC)/802.15.4 at the data link layer. They compared OHCA to QU-RPL and DCCC6 (duty cycle-aware congestion control for 6 LoWPAN) [45]. Like OHCA and QU-RPL, DCCC6 detects congestion using buffer occupancy, but uses a rate adaptation approach considering the presence of duty cycling. Simulation results showed that OHCA outperformed QU-RPL and DCCC6 in terms of throughput, fairness, end-to-end delay, energy consumption, and packet loss rate.
# A Voice-Enabled ROS2 Framework for Human-Robot Collaborative Inspection
## 2 Approach
### Quality Inspection
#### 2.3.2 Part Recognition  
For obtaining automatic vision-based defect identification, an object detection framework based on YOLOv4 (You Only Look Once version 4) model was developed. The basic components/functionalities of the proposed object detection framework are presented below.  
You only look once (YOLO) models [51] are a series of convolutional neural networks that are specialized in object detection and classification tasks in images or video frames. As result, the proposed object detection framework is extremely fast and generalizable while also offering high detection accuracy. To achieve this, eventually, the original input image is divided into an \(S\times S\) grid, meaning that the image is segmented in an equal number of rows and columns. Each grid cell is responsible for detecting the center of an object, if one appears inside of it, and produces a number of predefined anchor-bounding boxes \(N\), which are basically boxes of fixed sizes that are used in order to enhance the speed of the box regression for the model. Additionally, the model produces, for each object, a confidence score that indicates the model's certainty that an object is detected and that the bounding box for that object is accurate. Hence, the confidence score for a grid cell is zero if no object appears inside of it, and equal to the Intersection over Union (IoU) otherwise. IoU is basically the overlapping area of the predicted and the ground truth bounding boxesfor an object divided by the total area that they form. Each predicted bounding box consists of 5 parameters:  
* Two of them represent the center of the box (x, y);
* Two are for the width and height of it (w, h), relative to the shape of the image;
* One is for the box's confidence that basically represents the _IoU_.  
Additionally, the model provides a prediction for the object's class. This is represented by a 1D vector \(C\) with length equal to the number of all the classes that exist in the dataset. Thus, the final confidence score for a detected object encodes the probability of the predicted class and it is transformed into the following:  
\[P\Big{(}class_{predicted}\Big{)}\times IOU, \tag{1}\]  
Considering all the aforementioned information, the final prediction vector of each grid cell is of shape \(N*5+C\) and if that is expressed relative to the grid-separated image, it is transformed into the matrix \(SxSx(C+N*5)\), as it depicted in Figure 2. It is worth mentioning here that the grid is not applied to the original image but rather to the feature map of the final layer of a convolutional neural network referred to as backbone. The aforementioned procedure is visualized in relation to the corresponding image part in Figure 2.
# Research on Hard, Transparent and Hydrophobic Coating on PMMA Sheet
## 2 Materials and Methods  
### Coatings Preparation  
PMMA sheet with a size of 30 mm \(\times\) 40 mm \(\times\) 3 mm was used as substrate. Tetraethoxysilane (TEOS), methyltriethoxysilane (MTES), Dimethoxydimethylsilane (DMDS) were used as the precursor of silicon source. Absolute ethanol was used as a solvent. 3-(isobutenyloxy) propyltrimethoxysilane (KH570) was used as a coupling agent. Hydrophobic nano SiO\({}_{2}\) particles (size ranging 7-40 nm) were used as a surface modifier, and the added amounts were 0 (S0), 0.5 wt% (S05), 1 wt% (S1), 2 wt% (S2) correspondingly. Hydrochloric acid (HCl, 37 wt%) was used as a catalyst. The absolute ethanol and hydrochloric acid were purchased from China National Pharmaceutical Group Co., Ltd. (Beijing, China). The rest chemicals were purchased from Shanghai Macklin Biochemical Technology Co., Ltd. (Shanghai, China).  
Firstly, the nano SiO\({}_{2}\) particles were ultrasonically dispersed with absolute ethanol for 30 min. Then TEOS, MTES and DMDS with a mole ratio of 3:5:2 were added into the dispersion and mixed for 1 h. After that, a proper amount of deionized water and hydrochloric acid were added to the solution and fully mixed for 1 h at 70 \({}^{\circ}\)C, and then cooled to room temperature. Finally, the self-cleaning paint was obtained by adding the coupling agent KH570 and mixing it for 6 h at room temperature. A top pot spay gun was employed to deposit the coating on the PMMA substrate with an air pressure of 28 kPa. The sprayed sample was cured at room temperature for 24 h, pending testing.
## 4 Results and Discussion
### Challenges and Limitations  
Although RSNC-YOLO achieves better results than other YOLO models in the task of recognizing tuna on fishing boats in complex scenes, the proposed model still has some limitations, which can be attributed to two main aspects.  
Data limitations: Although the improved model demonstrates effectiveness in complex scenes, its general applicability was tested using recognition tasks on datasets that were self-collected and stylistically diverse. This revealed a common issue: when the background is homogeneous and the fish occupy a large portion of the image, the model's recognition performance significantly decreases. This suggests that the current model, which was trained on data selected from the Fishnet Open Image Database, is primarily suited to complex scenes. To enhance its performance across scenes of varying complexity, more relevant and diverse data need to be collected.  
Technical limitations: RSNC-YOLO employs instance segmentation to achieve more fine-grained recognition results, but this approach has its challenges. The use of instance segmentation in this study is not merely to segment the tuna itself but to leverage this finer-grained algorithm to provide the model with more accurate and detailed information during training, thus leading to improved recognition in complex scenarios. However, the use of instance segmentation also significantly increases the model's complexity. Because this method does not follow the original purpose of instance segmentation but is instead employed to improve accuracy during backpropagation parameter updates, there remains clear potential for further optimization.
## 2 Materials and Methods
### Estimation of GPP  
NIRv has been found to accurately capture both the seasonal and annual variation in GPP at flux sites [25; 26]. GPP correlates linearly with NIRv among different vegetation types (Table S1), and global GPP was estimated with high accuracy on a monthly basis by upscaling the relationships between NIRv and GPP [23]. In this study, we also use these correlations to determine the Suzhou monthly GPP based on NIRv, and the calculation of NIRv and GPP are shown in Equations (1) and (2):  
\[NIRv=\frac{NIR-R}{NIR+R}\times NIR \tag{1}\]  
\[GPP=a\times NIRv+b \tag{2}\]  
\(R\) and \(NIR\) are the red and the near-infrared bands, \(a\) and \(b\) are derived from linear regression, and the values can be found in Table S1. We used the MVC method to generate the monthly NIRv. Landsat-8 has a limited frequency of revisits, so some images were lost during the rainy season, i.e., in July and August. MODIS and Sentinel-2 had sufficient data to produce a full image each month.  
For different kinds of forests, such as deciduous broadleaf forests (DBF), evergreen broadleaf forests (EBF), evergreen needle forests (ENF), and mixed forests (MF), [PERSON] et al. [23] used different coefficients for calculating GPP. However, the land cover data of Sentinel-2 used in this study do not distinguish forest type. As a result, based on the studies of [49], changes in tree NDVI between summer and winter were examined to determine the forest types, including deciduous broadleaf forest, mixed forest and evergreen broadleaf forest, according to the criteria listed in Criteria 2 of Table 1. Additionally, we did not include evergreen needle forest in calculating GPP, for the reason that its distribution in Suzhou is limited and hard to separate from evergreen broadleaf forests by only the change in NDVI. Considering that the grassland and different kinds of forests in urban area are always integrated but still able to be identified according to dominated coverage of plants, vegetation types of urban forest in built area were also identified as grass (GRA), DBF, MF and EBF.  
In the built area, the contribution of urban forests to total regional carbon assimilation cannot be ignored as they are widely distributed in Suzhou. However, they are usually buried in lower-resolution imagery. Considering the fragmentation of urban forests, three different resolutions of remote images, i.e., MODIS, Landsat and Sentinel-2, were used to evaluate the ability to detect urban forests. For calculating GPP in urban forest, NDVI changes in the built area were used to determine vegetation types as shown in Criterions 1 and 2 of Table 1.
###### Abstract  
Total suspended sediment (TSS) is a water quality parameter that is used to understand sediment transport, aquatic ecosystem health, and engineering problems. The majority of TSS in water bodies is due to natural and human factors such as brought by river runoff, coastal erosion, dredging activities, and waves. It is an important parameter that should be monitored periodically, particularly over the dynamic coastal region. This study aims to monitor spatiotemporal TSS concentration over Teluk Lipat, Malaysia. To date, there are two commonly used methods to monitor TSS concentration over wide water regions. Firstly, field sampling is known very expensive and time-consuming method. Secondly, the remote sensing technology that can monitor spatiotemporal TSS concentration freely. Although remote sensing technology could overcome these problems, universal empirical or semiempirical algorithms are still not available. Most of the developed algorithms are on a regional basis. To measure TSS concentration over the different regions, a new regional algorithm needs to develop. To do so, two field trip was conducted in the study area concurrent with the passing of Landsat 8. A total of 30 field samples were collected from 30 sampling points during the first field trip and 30 samples from 30 samplings from the second field trip. The samples were then analyzed using an established method to develop the TSS algorithm. The data obtained from the first field trip were then used to develop a regional TSS algorithm using the regression analysis technique. The developed algorithm was then validated by using data obtained from the second field trip. The results demonstrated that TSS in the study area is highly correlated with three Landsat 8 bands, namely green, near-infrared (NIR), and short-wavelength (SWIR) bands, with R\({}^{2}\) = 0.79. The TSS map is constructed using the algorithm. Analyses of the image suggest that the highest TSSs are mainly observed along the coastal line and over the river mouth. It suggested that the main contributing factors over the study area are river runoff and wave splash.  
total suspended sediment; Landsat 8; algorithms; Teluk Lipat 2021 11 7082. [[https://doi.org/10.3390/app1157082](https://doi.org/10.3390/app1157082)]([https://doi.org/10.3390/app1157082](https://doi.org/10.3390/app1157082))
###### Abstract  
A low solvent consumption method for the determination of non-steroidal anti-inflammatory drugs (NSAIDs) in animal urine samples is studied. The NSAIDs were extracted with CH\({}_{2}\)Cl\({}_{2}\) by the ultrasound vortex assisted dispersive liquid-liquid microextraction (USVA-DLLME) method from urine samples, previously treated with \(\beta\)-glucuronidase/acrylsulfatase. After centrifugation, the bottom phase of the chlorinated solvent was separated from the liquid matrix, dried with Na\({}_{2}\)SO\({}_{4}\), and derivatized with N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) + trimethylchlorosilane (TMCS) (\(99+1\)). After cooling at room temperature, the solution was concentrated under nitrogen flow, and 1 uL of solution was analyzed in gas chromatography/ion trap-mass spectrometry (GC-IT-MS). The enrichment factor was about 300-450 times and recoveries ranged from 94.1 to 101.2% with a relative standard deviation (RSD) of \(\leq\)4.1%. The USVA-DLLME process efficiency was not influenced by the characteristics of the real urine matrix; therefore, the analytical method characteristics were evaluated in the range 1-100 ng mL\({}^{-1}\) (R\({}^{2}\)\(\geq\) 0.9950). The limits of detection (LODs) and limits of quantification (LOQs) were between 0.1 and 0.2 ng mL\({}^{-1}\) with RSD \(\leq\)4.5% and between 4.1 and 4.7 ng mL\({}^{-1}\) with RSD \(\leq\)3.5%, respectively, whereas inter- and intra-day precision was 3.8% and 4.5%, respectively. The proposed analytical method is reproducible, sensitive, and simple.  
non-steroidal anti-inflammatory drug (NSAID); urine; doping analysis; dispersive liquid-liquid microextraction (DLLME); gas chromatography mass spectrometry (GC-MS) 2020 10 5441
## 3 Applications
### Experimental Set Up  
3.1.1. Regular Breaking Waves on a Plane Beach  
The experiments by [PERSON] and [PERSON] (2018) were carried out in a wave flume 45 m long and 1 m wide, located at the Department of Civil, Environmental, Land, Building Engineering and Chemistry of the Polytechnic University of Bari, Italy (Figure 1). The flume has an initial flat bottom of 12 m extending from the wave paddle to section 73, while from section 73 up to the shoreline the bottom has a 1/20 slope, obtained with a wooden panel. The wavemaker is a piston-type, whose paddles produce the desired wave by moving the water mass according to a proper input signal. The wave elevations were measured with a resistance probe, while the instantaneous Eulerian velocities were acquired by a backscatter, two-component, four beam Laser Doppler Anemometer (LDA) system and a signal processor (Figure 1).  
Table 1 shows the main parameters of the two examined waves, in particular, the offshore wave height \(H_{0}\), the wave period \(T\), the offshore wavelength \(L_{0}\), the Irribarren number \(\zeta_{0}\), which has been estimated in section 76, located where the bottom is flat with water depth \(d\) equal to 0.70 m. Based on the Irribarren number \(\zeta_{0}\), the two regular tested waves were characterized by a spilling/plunging and plunging breakers, respectively.  
The sketch of Figure 2 shows the different sections named 76, 55, 49, 48, 47, 45, which have been used to make the comparisons between the simulated and measured free surface elevations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test** & \(H_{0}\) **(cm)** & \(T\) **(s)** & \(L_{0}\) **(m)** & \(d\) **(m)** & \(\xi_{0}\) & **Breaking Type** \\ \hline
**T1** & 11 & 2 & 4.62 & 0.70 & 0.37 & Spilling/plunging \\ \hline
**T2** & 6.5 & 4 & 10.12 & 0.70 & 0.74 & plunging \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameters of the analysed regular waves.  
Figure 1: Experimental apparatus: (**a**) a part of the wave channel and (**b**) the LDA probe.