esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Acquisition and Processing
### Construction of the KG for COVID-19  
Our drug KG contains more than 100,000 entities and more than 670,000 relationships. It is extremely difficult to perform a computation consuming model GCN on such a large-scale KG. In addition, the information of the drugs and proteins that are not related to COVID-19 in the KG will also interfere with drug repositioning. To reduce the calculation amount and improve the accuracy of the DDI prediction, we need to construct a KG for COVID-19 by extracting the related knowledge from the drug KG.  
In our drug KG, if a drug can treat a disease, there is usually a path with a distance less than 4 between them beside the direct connection. The path between them contains the information of why the drug can treat the disease. In addition, Att-GCN-DDI predicts DDIs through the similarity of topological structure, and diseases with similar topological structure are usually connected by the path with distance less than 4, such as disease-drug-disease, disease-gene-disease and disease-gene-gene-disease.  
Therefore, in this paper, we focus on the COVID-19 node, and select the drugs and disease nodes whose shortest path distance from the COVID-19 node is less than 4. After that, we use these drugs and disease nodes as the initial data to supplement the related gene, side effect and pathway nodes. Finally, the drug-disease drug-gene, gene-pathway, drug-drug, drug-side effect, gene-gene, and disease-gene relationships among nodes were supplemented too. Tables 1 and 2 show the number of entities and relationship of the KG for COVID-19.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Entity Type** & **Drug** & **Disease** & **Gene** & **Side Effect** & **Pathway** \\ \hline Number & 1470 & 752 & 1741 & 274 & 53 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of entities of each type in the KG for COVID-19.  
Figure 1: Data schema of our drug knowledge graph (KG).
# Topology and Response Surface Optimization of a Bicycle Crank Arm with Multiple Load Cases
## 2 Methods
### Topology Optimization Method
#### 2.2.4 Effect of Materials  
Lastly, to see the effect of different materials characteristics, two materials with different mechanical properties are used which are AL 7005 aluminum to represent the isotropic material and carbon fiber as the anisotropic material. These materials are chosen to represent the two most commonly used material in a bike crank, i.e., metal and composite. The compliance minimization with mass constraint is used to be able to compare the topological difference between the two materials with the same mass:  
\[\min\sum F_{pi}^{T}u_{i} \tag{6}\]  
Such that: \(m_{opt}\leq 0.5m_{0}\)  
The properties of the materials are shown in Table 3[27].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Case** & \multicolumn{3}{c}{**1**} & \multicolumn{3}{c}{**2**} & \multicolumn{3}{c}{**3**} \\ \hline \multirow{2}{*}{Angle (\(\theta\))} & 1 & 2 & 3 & 1 & 2 & 3 & 1 & 2 & 3 \\ \cline{2-10}  & 10\({}^{\circ}\) & 20\({}^{\circ}\) & 30\({}^{\circ}\) & 10\({}^{\circ}\) & 20\({}^{\circ}\) & 30\({}^{\circ}\) & 10\({}^{\circ}\) & 20\({}^{\circ}\) & 30\({}^{\circ}\) \\ \hline Weight (\(w\)) & 0.9 & 0.05 & 0.05 & 0.05 & 0.9 & 0.05 & 0.05 & 0.05 & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Uphill angle weighting scenario.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **AL 7005 Aluminum (Isotropic)** & **Carbon Fiber (Anisotropic)** \\ \hline Density & 2780 kg/m\({}^{3}\) & 1750 kg/m\({}^{3}\) \\ Young’s Modulus & 71,000 MPa & - \\ Young’s Modulus (x-direction) & - & \(3.95\times 10^{11}\) Pa \\ Young’s Modulus (y-direction) & - & \(6\times 10^{9}\) Pa \\ Young’s Modulus (z-direction) & - & \(6\times 10^{9}\) Pa \\ Poisson’s ratio & 0.33 & - \\ Poisson’s ratio (y-direction) & - & 0.2 \\ Poisson’s ratio (yz-direction) & - & 0.4 \\ Poisson’s ratio (xz-direction) & - & 0.2 \\ Shear Modulus & \(2.67\times 10^{10}\) Pa & - \\ Shear Modulus (y-direction) & - & \(8\times 10^{9}\) Pa \\ Shear Modulus (yz-direction) & - & \(2.14\times 10^{9}\) Pa \\ Shear Modulus (xz-direction) & - & \(8\times 10^{9}\) Pa \\ Ultimate Tensile Strength & 280 MPa & 3500 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model properties.  
In the ANSYS environment, the carbon fiber is modeled as an anisotropic material by selecting the composite materials option in the engineering data sources. The unidirectional fiber orientation along the x-direction of the crank arm is used which means all fibers are parallel to each other.
###### Abstract  
Hexavalent chromium (Cr(VI)) is a heavy metal that is highly soluble and exhibits toxic effects on biological systems. Nevertheless, it is used in many industrial applications. The adsorption process of Cr(VI), using activated carbon (AC), is under investigation globally. On the other hand, around six million tons of spent coffee is sent to landfill annually. In the spirit of cyclic economy, this research investigated the production of AC from spent coffee for the removal of Cr(VI) from wastewater. The AC was produced via pyrolysis process under a nitrogen atmosphere. Chemical activation using potassium hydroxide (KOH) occurred simultaneously with the pyrolysis process. The produced AC was tested as an absorber of Cr(VI). The best fitted kinetic model was the diffusion-chemisorption model. A 24-h adsorption experiment was carried out using a solution with a pH of 3 and an initial Cr(VI) concentration of 54.14 ppm. This resulted in an experimental maximum capacity of 109 mg/g, while the theoretical prediction was 137 mg/g. It also resulted in an initial adsorption rate (\(r_{i}\)) of 110 (mg/(g h)). The Brunauer-Emmett-Teller surface area (S\({}_{\text{gBET}}\)) was 1372 m\({}^{2}\)/g, the Langmuir surface area (S\({}_{\text{gLang}}\)) was 1875 m\({}^{2}\)/g, and the corrugated pore structure model surface area (S\({}_{\text{gCPSM}}\)) was 1869 m\({}^{2}\)/g. The micropore volume was 84.6%, exhibiting micropores at D\({}_{\text{micro1}}\) = 1.28 and D\({}_{\text{micro2}}\) = 1.6 nm. The tortuosity factor (\(\tau\)) was 4.65.  
activated carbon; spent coffee; waste coffee; biowaste vaporization; hexavalent chromium adsorption; chromium removal kinetics; biowaste activated carbon characterization
# A Hierarchical Fusion SAR Image Change-Detection Method Based on HF-CRF Model
## 2 Materials and Methods
### D-DRUNet Fusion Semantic Segmentation Network
#### 2.1.1 Dual-Encoder and Single-Decoder Structure  
The proposed method utilizes the log-ratio difference map and mean-ratio difference map as dual encoder inputs, enabling the network to extract change features simultaneously from different modal difference images. The method employs different fusion strategies at different stages of image encoding and decoding. The encoder consists of a convolution operation and a downsampling operation, where the convolution process characterizes the image information and the downsampling process obtains the contextual information of the image. The shallow features obtained at the two-way encoding stage retain rich detailed information, while the deep features ensure semantic structure integrity. The single-way decoder side performs multiple upsampling operations for the fused features to recover the compressed feature maps in the spatial dimension in the original input size, layer by layer. The dual encoder-single decoder structure effectively improves the information perception capability of the network by designing a multimodal feature extraction and fusion approach.
## 3 Challenges
### Quality of Service (QoS)  
In conventional WSNs, the QoS is generally characterized by four parameters: bandwidth, delay, jitter and reliability. To avoid hazardous consequences in critical applications, WSNs need to maintain an adequate level of QoS. QoS support is a challenging issue due to resource constraints, such as processing power, memory, and power sources in wireless sensor nodes. This is more challenging in CR-WSNs because in addition to the challenges in WSNs, it has one more challenge to protect the rights of PUs to access the incumbent spectra. PU's communication should be interference free with the SUs. This is more challenging because it is difficult to predict the PU's arrival on the channel. A miss-detection of the primary signal and a false alarm can cause additional challenges.
# Monitoring the Dynamics of Ephemeral Rivers from Space: An Example of the Kuiseb River in Namibia
## 4 Results and Discussion
### Variations at the Klipneus Kuiseb River Tributary
The large flood in 2021, which lasted 7 days, recharged aquifers and raised underground water tables along the Kuiseb River and in its floodplains. The interferogram (Figure 8f) shows that it is likely to have increased the water level in the Klipneus tributary too. A possible explanation of the process is illustrated in Figure 9. In Figure 9a the tributary is outlined in red, with the Kuiseb floodplains in green and water flow along the main channel in blue. Figure 9b,c are photographs of a flood event. Figure 9d represents an idealised topographic profile along a transect in Figure 9a (in white) across the Kuiseb River bed and the Klipneus tributary. During a flood event, water will infiltrate into the river bed (at a rate of 8-12 mm/h, after [3]) and from overspill on the floodplain, but some water will be lost through evapotranspiration, evaporation, and pumping water for human activities. After a flood event, as presented in Figure 9e, groundwater levels will rise due to the recharge from infiltration, which would be the origin of variations in returns from the radar signal (coherence) in the tributary. As moisture levels rise and come closer to the surface, it will produce a change in the interferometric phase, i.e., a drop in coherence between different dates of acquisition. Groundwater level rise is the cause of an increase in surface moisture, resulting in a decrease in the coherence measured by the Sentinel-1 data. Indeed, the phase depends on the dielectric properties of the soil and will be sensitive to the presence of water/moisture. Figure 7: (**a**) The tributary at Klipneus in the central part of our lower Kuiseb River study area, (**b**) changes of the amplitude \(\alpha_{0}\) (VV polarization) over the 2016–2022 period in the tributary, (**c**) corresponding NDVI map from 2018, and (**d**) NDVI map from 2021. Figure 8: Coherence maps associated with the tributary area at Klipneus in Figure 7a, adjacent to the Kuiseb river channel. (**a–d**) are coherence maps associated with a local rain event in 2018; (**e–h**) are coherence maps associated with a flood event in 2021.
## 3 Results
### Transport of Cells
#### 3.2.1 Transport of Cells Without Obstacles  
Figure 11a-d show the transportation of one yeast cell under the manipulation of the microtool operated by optical tweezers. In this experiment, no obstacle was situated in the workspace, and the desired position was marked by a star. The target cell was pushed along a straight path to the desired position at a velocity of 2.5 \(\upmu\)m/s. During transportation, the cell remained inside the V-shaped head. Figure 11e shows the absolute cell position errors during the manipulation, the maximum of which is less than 2.5 \(\upmu\)m.  
Figure 11: Experiments of cell transport without obstacles in the workspace. (**a**) t = 0 s: the cell in the initial position. (**b**) t = 5 s: the cell in transport. (**c**) t = 10 s: the cell in transport. (**d**) t = 15 s: the cell was successfully transported to the desired position. (**e**) Absolute position errors of the cell in transport.  
Figure 10: Experiments of rotation movement of the microtool. (**a**) Initial direction. (**b**) Desired direction. (**c**) Desired and actual movement paths. The black dot denotes the desired position of \(O_{R}\) and the red dot indicates the actual path of \(O_{R}\). (**d**) Absolute directional errors.
# Analysis of BDS/GPS Signals' Characteristics and Navigation Accuracy for a Geostationary Satellite
## 4 Navigation Performance
### Real-Time Orbit Determination Accuracy
#### 4.2.2 Analysis Results  
The navigation solutions are generated by a real-time orbit determination software using flight GNSS observations and broadcast ephemerides. Orbit determination filter based on EKF updates with observations at step size of 20 s. The RMS differences of position  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Description** \\ \hline Gravity model & EGM 2008, 50 \(\times\) 50 \\ Luni-solar gravitation & Low precision model, Moon and Sun’s position are computed via analytic method \\ Earth tides & Low precision model, k20 solid only \\ Solar radiation pressure & Cannonball model, fixed effective area, \(C_{R}\) as estimation state \\ Integrator & 4 th-order fixed step Runge–Kutta \\ Integration step size & 20 s \\ Empirical acceleration & Dynamic model compensation (DMC), \(\tau=60\) s, \(\sigma_{\text{R}}\):\(\sigma_{\text{A}}\):\(\sigma_{\text{C}}=200\):50:50 nm/s\({}^{2}\) \\ Observations & Pseudorange \\ Pseudorange noise & 10 m \\ GNSS orbit and clock & Broadcast ephemeris \\ \hline \hline \end{tabular}
\end{table}
Table 6: Models and parameters of onboard orbit determination used in TJS-5 mission.  
and velocity between real-time orbit determination solutions with different pseudorange combination weights and the reference of the precision orbit determination solutions as listed in Table 7. According to the 3D results, the highest position and velocity accuracy are given at the weight of 1:1. In that case, the RMS of position differences is 5.55 m and that of position differences is 0.697 mm/s. With the increase of the weight of BDS observations, although the accuracy in x-axis direction has some improvements, the accuracy in y-axis direction and z-axis direction become worse. For instance, when GPS and BDS pseudorange combined at a weight of 1:2, the RMS of position differences in x-axis direction reduce from 7.24 m to 4.09 m. However, the corresponding differences in y-axis and z-axis direction increase by 1.06 m to 0.37 m. In detail, we give the position differences between real-time orbit determination solutions using GPS + BDS observations combinations at a weight of 1:1 and only GPS observations, and the reference of the precision orbit determination solutions, as shown in Figure 9.  
Figure 9: Position differences (x-axis, y-axis, and z-axis) over two days between real-time orbit determination solutions using GPS and GPS + BDS pseudorange with weight ratio of 1:1 and the reference of the precision orbit determination solutions.
## 3 Proof-of-Concept Methodology  
### Acute Toxicity Bioassay  
We performed bioassays utilizing the standard methods described above. The assays were conducted from December 2020 to January 2021 at the University of Florida Honey Bee Research and Extension Laboratory (UF HBREL) in Gainesville, FL, USA. Containers of adult SHBs feeding on pollen or pupating SHBs in damp soil were provided from a lab-reared colony maintained at the USDA Center for Medical, Agricultural, and Veterinary Entomology (USDA-CMAVE) in Gainesville, FL, USA.  
We used 100% acetone as a solvent control to dilute all compounds tested in our bioassays (all the compounds were soluble in acetone). We performed preliminary range finding bioassays by testing three concentrations of each test compound, with each concentration being five times higher than the proceeding one (coumaphos--100, 500, 2500 ug/g; acetamiprid and fipronil--10, 50, 2500 ug/g; thiamethoxam--500, 2500, 12,500 ug/\(\mu\)L). We used knowledge from previous assays and existing literature reporting the toxicity of these compounds on other beetle species and honey bees to select the initial range-finding concentrations [15, 20, 21, 22]. With this information, we calculated the LC\({}_{50}\) values for coumaphos, acetamiprid, and fipronil by determining SHB mortality at ranges of 500-2500 ug/g, 12.5-300 ug/g, and 1.25-2.75 ug/g compound, respectively. Every round included at least five negative (nothing added), five solvent (acetone), and three positive (thiamethoxam/fipronil) controls. We used thiamethoxam as an alternative positive control (2500 ug/g) during the refinement process but did not generate an LC\({}_{50}\) value.
# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
## 6 Conclusions
Conceptualization, P.A.-A. and S.L. ; methodology, P.A.-A., S.L., S.D.P., M.S.-A., D.H.-L. and D.G.-A. ; software, P.A.-A. ; validation, P.A.-A., S.D.P. and S.L. ; formal analysis, M.S.-A. ; investigation, D.H.-L., D.G.-A. and M.S.-A. ; resources, P.A.-A., D.H.-L. and D.G.-A. ; data curation, M.S.-A. ; writing--original draft preparation, P.A.-A. ; writing--review and editing, D.H.-L., D.G.-A., M.S.-A. and S.D.P. ; visualization, S.D.P. ; supervision, S.L., D.H.-L. and D.G.-A. ; project administration, S.L. ; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript. This research was funded by Catedra Iberdrola VIII Centenary of the University of Salamanca; the Junta de Castilla y Leon with the Fondo Social Europeo through programs for human resources (grant number EDU/601/2020); and the Ministry of Education, Culture and Sports (Government of Spain) through human resources (grant number FPU19/06034). The University of Salamanca acknowledges the TREEADS project, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 101036926. Content reflects only the author's view and the European Commission is not responsible for any use that may be made of the information it contains. Not applicable. **Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: SURFRAD data [[[https://gml.noaa.gov/dv/data/index.php?category=Radiation&parameter_name=Surface+Radiation](https://gml.noaa.gov/dv/data/index.php?category=Radiation&parameter_name=Surface+Radiation)]([https://gml.noaa.gov/dv/data/index.php?category=Radiation&parameter_name=Surface+Radiation](https://gml.noaa.gov/dv/data/index.php?category=Radiation&parameter_name=Surface+Radiation))], Landsat data [[[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/))] and ABOVE data [[[https://search.earthdata.nasa.gov/search/granules?p=C21313058037-ORNL_CLOUDB&pg](https://search.earthdata.nasa.gov/search/granules?p=C21313058037-ORNL_CLOUDB&pg)]([https://search.earthdata.nasa.gov/search/granules?p=C21313058037-ORNL_CLOUDB&pg](https://search.earthdata.nasa.gov/search/granules?p=C21313058037-ORNL_CLOUDB&pg))[0][v]=f&pg[0][gsk]=-start_date&fpi)=ABoVE&as[project][0]=ABoVE&tl=1703002974.8631!6&lat=56.36611904614044&long=-172.6875] (all accessed on 17 December 2023). **Acknowledgments:** The authors want to thank the Spanish Ministry of Education, Culture and Sports for providing an FPU grant (Training Program for Academic Staff) to the corresponding author of this paper. The same acknowledgement is given to Junta de Castilla y Leon. Authors would like to thank Iberdrola Espana SAU and University of Salamanca for their support in human and material resources given through Catedra Iberdrola VIII Centenario. **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods  
Ten able-bodied, right-handed subjects volunteered for this study (age 27.3 \(\pm\) 2.1 years), without any history of neurological or upper limbs pathologies which could affect hand motions. All the subjects signed written informed consent prior the beginning of the test and the experimental procedure was approved by the local expert committee, in accordance with the Declaration of Helsinki.  
A total of 14 gestures (Figure 1) were selected, and following the protocol reported in [20,41], each volunteer performed six consecutive repetitions of the same hand movement, lasting for about 3 s, followed by an equal epoch of resting. Periods of gesture maintenance and resting were selected by the subjects following the indications reported on a screen placed in front of them during the entire acquisition. Thus, each volunteer performed 84 repetitions, for a total of 252 analyzed signals, partitioned as follows: the first set (set 1, Figure 1a) contained 6 gestures involving finger movements; i.e., thumb up (TU), flexion of ring and little finger and extension of the other fingers (miming number 3, N3), thumb opposing base of little finger (miming number 4, N4), hand opening (miming number 5, N5), fingers flexed in the fist (N0) and pointing index (miming number 1, N1). The second set (set 2, Figure 1b) of gestures involved wrist movements: wrist supination (WS), wrist pronation (WP), wrist flexion (WF) and wrist extension (WE). In the last set (set 3, Figure 1c), the following grasping gestures were considered: large diameter grasping (LG); small diameter grasping (SG, also known as power grasping); ring grasping (RG), wherein a circular object is held by using the thumb and the index finger; and pinch grasping (PG), wherein a small object is grabbed by using the thumb and the index finger.  
Figure 1: Graphical representation of the 14 gestures considered in this study, sorted into three sets: set 1 encompasses fingers movements (**a**), set 2 includes wrist movements (**b**) while within set 3 grasping gestures are considered (**c**).  
sEMG signals were acquired through a single Myo armband, a commercial device equipped with 8 single differential sEMG sensors and a 9-axial inertial measurement unit. The device has a fixed sampling rate of 200 Hz (resolution 8 bit) and data were streamed through a Bluetooth connection and stored for offline processing. The armband was worn by each subject on the forearm of the dominant upper limb, with the first sensor placed in relation to the radio humeral joint, following the placement recommendations reported in [20]. The device did not require any calibration prior the beginning of the test, and the adherence of the armband to the subject's forearm was checked before data acquisition; the device was made to be firm on the arm (Figure 2).