esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuel-Efficient Road Classification Methodology for Sustainable Open Pit Mining
## 4 Results and Discussion
### Economic Analysis and Discussion of Road Classification
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Loaded Trucks**} & \multicolumn{3}{c}{**Unloaded Trucks**} \\ \hline \multirow{3}{*}{**IRI for Every**} & \multirow{3}{*}{**Fuel Consumption**} & \multirow{3}{*}{\begin{tabular}{c} **Fuel** \\ **Consumption** \\ **for 150 m (L)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Cost** \\ **(CNY)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Fuel Consumption** \\ **(L/100 km)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Fuel** \\ **(CNY)** \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} **Fuel** \\ **(L/100 km)** \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} **Cost** \\ **(L/100 km)** \\ \end{tabular} } \\ \hline \multirow{3}{*}{Road 1} & 25.95 & 2068.05 & 3.10 & 22.96 & 1528.39 & 2.29 & 16.97 \\  & 21.57 & 2005.66 & 3.01 & 22.26 & 1485.49 & 2.23 & 16.49 \\  & 19.04 & 1969.71 & 2.95 & 21.86 & 1460.78 & 2.19 & 16.21 \\  & 17.41 & 1946.49 & 2.92 & 21.61 & 1444.81 & 2.17 & 16.04 \\  & 18.62 & 1963.76 & 2.95 & 21.80 & 1456.68 & 2.19 & 16.17 \\  & 23.57 & 2034.15 & 3.05 & 22.58 & 1505.08 & 2.26 & 16.71 \\  & 26.15 & 2070.99 & 3.11 & 22.99 & 1530.41 & 2.30 & 16.99 \\  & 18.70 & 1964.86 & 2.95 & 21.81 & 1457.44 & 2.19 & 16.18 \\  & 20.57 & 1991.46 & 2.99 & 22.11 & 1475.73 & 2.21 & 16.38 \\  & 15.07 & 1913.09 & 2.87 & 21.24 & 1421.85 & 2.13 & 15.78 \\  & 19.35 & 1974.15 & 2.96 & 21.91 & 1463.83 & 2.20 & 16.25 \\ \hline \multirow{3}{*}{Road 2} & 13.45 & 1890.09 & 2.84 & 20.98 & 1406.03 & 2.11 & 15.61 \\  & 28.43 & 2103.42 & 3.16 & 23.35 & 1552.70 & 2.33 & 17.23 \\  & 20.79 & 1994.61 & 2.99 & 22.14 & 1477.90 & 2.22 & 16.40 \\  & 19.11 & 1970.66 & 2.96 & 21.87 & 1461.43 & 2.19 & 16.22 \\  & 21.24 & 2001.02 & 3.00 & 22.21 & 1482.30 & 2.22 & 16.45 \\  & 28.89 & 2109.89 & 3.16 & 23.42 & 1557.15 & 2.34 & 17.28 \\  & 21.81 & 2009.18 & 3.01 & 22.30 & 1487.91 & 2.23 & 16.52 \\  & 11.40 & 1860.91 & 2.79 & 20.66 & 1385.98 & 2.08 & 15.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Calculation results of road fuel costs for the case study. Figure 14: Test roads. Therefore, it is evident that large open-pit coal mines could potentially save millions of CNY by constructing roads in compliance with the established fuel-efficient roads standard. Furthermore, enhancing road quality can lead to reduced vehicle maintenance costs, such as those associated with tires.
###### Abstract  
Unmanned aerial vehicles (UAVs), commonly known as drones, are a rising topic in remote sensing technologies for structural health monitoring. With technology advancement in cameras, the visual inspection method using drones is gaining much attention in the field of civil engineering. However, although visual inspection methods are feasible for finding cracks in structures, the limitations of image processing for finding internal damage or small defects cannot be ignored. To overcome this problem, a possible application concept of UAV, combined with a vibration-based non-destructive health monitoring method, is proposed. The idea is for the drone to temporarily attach the piezoelectric transducer onto a specific region where excitation and data acquisition occurs simultaneously. This eliminates the need for a structure to be covered with hundreds of sensors for monitoring, as this concept uses a single piezoelectric transducer for monitoring a structure. The proposed work offers new areas of research by converging UAV with a vibration-based method, as associated problems are required to be solved.  
unmanned aerial vehicle (UAV); drone; structural health monitoring (SHM); piezoelectric transducer; non-destructive testing +
Footnote †: journal: Applied Sciences
# Feasibility of a Drug-Releasing Radiofrequency Ablation System in a Porcine Liver Model
## 2 Materials and Methods
### In Vivo Drug-Relasing RFA Procedure  
This study was approved by the local Institutional Animal Care and Use Committee and performed following the guidelines of the National Institutes of Health for the human handling of animals. Nine RFA zones (three ablations per pig) were performed by one radiologist with >10 years of experience under intraoperative ultrasonographic guidance to minimize potential variations in the procedural outcomes. An RF electrode was placed in the liver under real-time ultrasound guidance away from intrahepatic vessels, interlobar fissure, or the liver capsule so that the RFA zone would not be influenced by these structures. The distance between the center of each ablation zone was kept at least 5 cm apart to avoid ablation zone attachment.  
The pigs were anesthetized using an intramuscular injection of tiletamine-zolazepam mixture (7.5 mg/kg; Zoletil 50; Virbac Korea Co., Seoul, Korea) and xylazine hydrochloride (2 mg/kg; Rompun; Bayer Korea Ltd., Ansan, Korea). Moreover, anesthesia was maintained with inhaled isoflurane of 1.5% in 100% oxygen (Forane Solution; Choongwae Pharma Co., Seoul, Korea) by endotracheal intubation. Cardiac status with electrocardiography, including heart rate, blood pressure, and oxygen saturation was monitored throughout the procedure. The pigs were placed in the supine position and vital signs were monitored. Two ground pads were placed on both shaved thighs connected in parallel to the RF generator. The liver was exposed by laparotomy after the epigastric  
Figure 2: The radiofrequency ablation (RFA) system configuration. (**a**) A three-channel radiofrequency ablation system, which can allow the use of multiple electrodes in either monopolar or bipolar modes. (**b**) An internally cooled wet electrode with a 2-cm active tip. (**c**) The magnified image of the DRCWE tip with a side hole (white arrow).  
area was sterilized. Moreover, the DRCWE was inserted into the liver parenchyma under ultrasonographic guidance (Figure 3a,b). The baseline power was set at 30 W. Ablation was continued with an increase of 10 W per minute until roll-off occurred. The power passively decreases to <10 W when tissue impedance rises above 200 \(\Omega\). Simultaneously, 18F-fluorodeoxyglucose (18F-FDG) was slowly injected into the liver by an automatic infusion syringe at a speed of 3 cm\({}^{3}\)/min to evaluate the spread of 18F-FDG by positron emission tomography (PET)/magnetic resonance imaging (MRI) examination (Figure 3c). After the RF energy was fully delivered for 10 min in the penetrated liver parenchyma, all electrodes were slowly removed when the electrode temperature reached approximately 70 \({}^{\circ}\)C to prevent tract bleeding along the tip. Three pigs were euthanized with a lethal injection of potassium chloride immediately after the procedure.
## 2 Materials and Methods
### Odometry Errors: Systematic and Non-Systematic
#### 2.3.2 Non-Systematic Odometry Errors  
Non-systematic odometry errors are not possible to predict, because they are originated by unpredictable features of the environment such as terrain irregularities, terrain obstacles, structural wheel slippage during specific motions, wheel slippage originated by the terrain conditions, or the application of external forces to the mobile robot. However, this paper is applied to a mobile robot operating in perfect flat indoor terrains, so the assumption is that the motion will not be affected by non-systematic odometry errors.
## 4 Discussion
### Integration with BIM Models
[19] developed an on-site safety monitoring system that integrates BLE-based localisation, employing BIM for risk detection and cloud communication. The system identifies hazardous areas in the BIM model, either automatically or manually, and tracks the real-time location of workers to assess their exposure to risk [32]. It also ensures correct harness use in high-risk areas [31], with results shared via the cloud for effective safety management. In the study developed by [PERSON] et al. [39], the worker's internal position was defined based on the smartphone's GPS, and in the BIM model, the safety manager created the fall risk map from a height. The location data obtained from the smartphone is mapped and displayed as a red dot, and the system sends an alert to the worker's smartphone, depending on the position. The RTLS positioning system used in the study [42] consists of reference tags and anchors associated with workers and danger zones, which transmit information via wireless signals. The synchronised 3D model was used to receive the location data in real time and process the information to signal when a worker is in a danger zone previously defined in the model. This system allows an alert to be sent to the workers via the helmet-mounted tags when they approach a danger zone. The studies [50; 53; 68] integrated BIM with computer vision to transform the information collected on site and visualise the potential risks that workers are exposed to at any given moment in the generated 3D model, known as the virtual safety model. When integrating monitoring technologies with BIM models, in order to verify the potential risks in the model, it is necessary to ensure that the construction model is up to date, i.e., that the digital twin corresponds to the progress of the work and that there are no discrepancies between the actual condition and the information in the BIM.
# IoT Group Membership Management Using Decentralized Identifiers and Verifiable Credentials
## 3 Design
### CoAP Response Verification  
Upon receiving a CoAP response, a CoAP client executes the following steps:  
1. Initially, it extracts the _membership_ VC included in the _membership_ option and verifies its proof using the appropriate _assertion_ key defined in the DID document of the group _administrator_. If the proof includes an expiration time, the client verifies that this time has not passed. If all verifications are successful, the client validates that the _groups_ claim includes the requested group and that the _sub_ claim includes the \(O\textit{{owner}}_{DID}\) of the endpoint that responded.
2. The client extracts the DID document of \(O\textit{{owner}}_{DID}\) included in the _endpoint_ option and verifies its proof. Then, it retrieves from that DID document the public key which has been used as the authentication key.
3. The client verifies that the _attestation_ option includes the correct values for the URL, token, and sha-256 fields. Then, it verifies the digital signature of that option using the extracted authentication key.  
The first step of this process verifies that the received response has been generated by an endpoint authorized by the group administrator to provide responses for that particular CoAP URI. The next steps of this process verify the integrity and the authenticity of the received response.
## 4 Analysis and Comparison
### Comparison of Algorithms
#### 4.2.1 Detection Rate  
The most direct criterion for judging the quality of a detection algorithm is the detection rate. To minimize the \(P_{f}\), the detected decision is made when the detected position is a half symbol (eight samples for 802.11 ah in this paper) around the real position, which is the index of 101 in our experiments. The result is shown in Figure 19. Overall, the detection rate increases with the increase of SNR. When the SNR is large, the gap between several algorithms is not large, but when the SNR is small, the gap is obvious. Cross-correlation algorithm provides a very high detection rate, which is close to 1. The detection rate of signal energy detection is relatively high. Auto-correlation is centered and the double sliding window detection algorithm is the lowest. The detection rates of the latter two algorithms are below 50% when SNR = 0.  
Figure 17: Complex decision rule for cross-correlation.  
Figure 18: Detection rate comparison of different decision rules for cross-correlation.
## 3 Methodology  
### Relationship between the Satellite-Observed Parameters and the Maximum Wind Speed  
In reference to [PERSON] and [PERSON] (2016), the TB parameters of the SSMIS microwave radiometer and sea surface wind speed (SSW) parameters of HY-2A microwave scatterometer in concentric circles and annuli from the TC center were calculated, and then compared with the MSW using the CMA TC best track data. TB parameters are computed for each TB of 19, 22, 37, and 91 GHz over the ocean.  
Similar to [PERSON] et al. (2017), we used two ways for the selection of coverage area when calculating each parameter. One way is a concentric circle, with different radii every 0.25\({}^{\circ}\) from 0.5\({}^{\circ}\) to 2.5\({}^{\circ}\). Another way is an annuli enclosed by circles every 0.25\({}^{\circ}\), such as annulus with a 1\({}^{\circ}\) inner radius and 1.25\({}^{\circ}\) outer radius (Figure 3). The center of the TC is set as the center of the circle, and it can be obtained by interpolation of the 6-hourly best track data. It should be noted that the different sizes of TCs and the eyewall replacement cycles may cause some intensity estimation errors. Therefore, the sizes of tropical cyclones and eyewall replacement cycles should be considered in future work. Then, the maximum, minimum, mean, and standard deviation (STD) of TB and SSW, and the ratio of pixels over the threshold (RAPT) of TB are computed in the computation coverage area. For RAPT, the minimum and maximum threshold temperatures for each frequency channel are 180 K and 270 K, respectively. There are, in total, 1785 parameters that are calculated in this process, so we formulated unified rules to name them. For the TB parameters obtained by SSMIS microwave radiometer, frequency, and polarization are first displayed (for instance, TB19H for horizontal polarized TB of 19 GHz). Then, the parameter type (MAX, MIN, MEAN, STD, MAX-MIN, and MAX-MEAN) was displayed (TB19H_MEAN). Lastly, a type of computation coverage (A or C) and radius follows. Computational regions are written as 'A' to represent annuli, followed by inner radius and outer radius for annuli (Axxxxxx, the first three x (xxx) are used for the position of the inner circle, the next three x (xxx) are for the position of the outer circle, i.e., TB19H_MEAN_A100125 represents an annulus whose inner radius is 1.0\({}^{\circ}\) and outer radius is 1.25\({}^{\circ}\)), and 'C' to represent a concentric circle, followed by the radius of the circle (Cxxx, 3 x (xxx) is used for circle radius, i.e., TB19H_MEAN_C100 represents a circle whose radius is 1.0\({}^{\circ}\)). For the SSW parameters obtained by the HY-2A microwave scatterometer, because they do not have as much frequency polarization information, they do not have a first step in naming rules like TB parameters do (SSW_MEAN_A100125 or SSW_MEAN_C100).
# Effects of _Gynura bicolor_ on Glycemic Control and Antioxidant Ability in Predicates
## 3 Results
### Anthropometric Measures, Blood Pressure, Lipid Profile and Liver Function  
Anthropometric measures included weight, BMI, body fat, fat-free mass, waist circumference, hip circumference and waist:hip ratio. There were no differences at baseline, Week 4, Week 8 and Week 12 between groups or within groups (Table 4). Blood pressure, TC, HDL-C, LDL-C, AST, and ALT levels did not differ between groups or within groups at baseline, Week 4, Week 8 and Week 12 (Tables 5 and 6). \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Control** & **GB** \\ \hline Fasting plasma glucose (mg/dL) & & \\ Week 0 & \(111.22\pm 6.43\) & \(125.00\pm 3.83\) \(\lx@sectionsign\) \\ Week 8 & \(100.11\pm 2.30\) & \(108.22\pm 2.90\) \(\lx@sectionsign\)* \\ Week 12 & \(101.78\pm 3.60\) & \(106.00\pm 3.51\) \(\lx@sectionsign\) \\ \(\triangle\)0–8 week & \(-11.11\pm 6.04\) & \(-16.78\pm 2.72\) \\ \(\triangle\)0–12 week & \(-9.1\pm 18.8\) & \(-19.8\pm 9.3\) \(\lx@sectionsign\) \\ \(\triangle\)8–12 week & \(2.1\pm 9.6\) & \(-1.4\pm 8.1\) \\ Fasting insulin (mU/L) & & \\ Week 0 & \(10.60\pm 0.91\) & \(12.87\pm 1.32\) \\ Week 8 & \(12.10\pm 1.61\) & \(9.89\pm 0.93\) \\ Week 12 & \(8.99\pm 0.87\) & \(9.00\pm 1.03\) \(\lx@sectionsign\) \\ \(\triangle\)0–8 week & \(1.50\pm 1.30\) & \(-2.98\pm 1.37\) \(\lx@sectionsign\) \\ \(\triangle\)0–12 week & \(-1.30\pm 2.7\) & \(-4.20\pm 4.8\) \\ \(\triangle\)8–12 week & \(-2.30\pm 3.4\) & \(-1.20\pm 3.8\) \\ HbA1C (\%) & & \\ Week 0 & \(5.79\pm 0.12\) & \(6.41\pm 0.17\) \(\lx@sectionsign\) \\ Week 8 & \(5.76\pm 0.11\) & \(6.30\pm 0.13\) \(\lx@sectionsign\) \\ Week 12 & \(5.69\pm 0.11\) & \(6.14\pm 0.14\) \(\lx@sectionsign\) \\ \(\triangle\)0–8 week & \(-0.03\pm 0.05\) & \(-0.11\pm 0.08\) \\ \(\triangle\)0–12 week & \(-0.10\pm 0.11\) & \(-0.26\pm 0.28\) \\ \(\triangle\)8–12 week & \(-0.07\pm 0.10\) & \(-0.13\pm 0.18\) \\ HOMA-IR & & \\ Week 0 & \(2.97\pm 0.39\) & \(3.95\pm 0.40\) \\ Week 8 & \(3.02\pm 0.44\) & \(2.63\pm 0.25\) \(\lx@sectionsign\) \\ Week 12 & \(2.30\pm 0.27\) & \(2.34\pm 0.26\) \(\lx@sectionsign\) \\ \(\triangle\)0–8 week & \(0.05\pm 0.31\) & \(-1.32\pm 0.42\) \(\lx@sectionsign\) \\ \(\triangle\)0–12 week & \(-0.52\pm 0.85\) & \(-1.52\pm 1.52\) \\ \(\triangle\)8–12 week & \(-1.00\pm 1.59\) & \(-0.36\pm 0.96\) \\ QUICKI & & \\ Week 0 & \(0.33\pm 0.01\) & \(0.31\pm 0.00\) \\ Week 8 & \(0.33\pm 0.01\) & \(0.33\pm 0.00\) \\ Week 12 & \(0.34\pm 0.01\) & \(0.34\pm 0.00\) \\ \(\triangle\)0–8 week & \(-0.00\pm 0.01\) & \(0.02\pm 0.01\) \(\lx@sectionsign\) \\ \(\triangle\)0–12 week & \(-0.02\pm 0.11\) & \(-0.01\pm 0.11\) \\ \(\triangle\)8–12 week & \(-0.02\pm 0.11\) & \(0.00\pm 0.11\) \\ \hline \hline \end{tabular} Values are mean \(\pm\) SEM (n = 9), \(\lx@sectionsign\)\(p<0.05\) between groups by Mann–Whitney U test; \(\lx@sectionsign\)\(p<0.05\) compared with Week 0 by Mann–Whitney U test, HOMA-IR, homeostasis model assessment-insulin resistance; QUICKI, quantitative insulin sensitivity check index.  
\end{table} 
Table 3: Effects of _Gynura bicolor_ on glycemic control profile.
## 1 Introduction
Considering the fluid memory effects, the self-excited forces (and hence aeroelastic responses) of the bridge deck depend not only on the motion of the current time step, but also on the history of the previous time steps, which cannot be efficiently captured by conventional feedforward ANN with a fixed input length [23]. On the other hand, the recurrent neural networks (RNNs) take the previous outputs as part of the inputs of the current time step. However, theoretical and empirical evidence have shown that the traditional RNN network cannot reliably utilize the information of more than 10 previous time steps [24,25]. To overcome the gradient vanishing or explosion, long short-term memory (LSTM) was introduced to augment the network. Several studies have shown LSTM as presenting great promise in modeling the complex nonlinear dynamic systems and the prediction of long-time series [26,27,28]. [PERSON] et al. [29] used an LSTM neural network to predict the LCO of the airfoil in transonic flow, indicating that the LSTM network has the ability to learn the complex nonlinear system with various structural parameters. [PERSON] et al. [30] successfully captured the nonlinear post-flutter behavior of a bridge deck at various wind speeds by utilizing the LSTM networks. Furthermore, [PERSON] and [PERSON] [31] introduced a knowledge-enhanced LSTM network to enhance the training efficiency and simulation accuracy. [PERSON] et al. [32] and [PERSON] et al. [33] also introduced a new loss function to the original LSTM network for enhancing the training efficiency and prediction stability. The works mentioned previously motivated this current study to develop a reduce-order model to capture the nonlinear features of the post-flutter of the bridge decks under different leading-edge angles. In this study, CFD was first used to simulate nonlinear aeroelastic forces to investigate the LCO phenomenon of bridge decks with various aerodynamic configurations. Then, the aerodynamics datasets were generated by high-fidelity CFD simulations, where the deck motion was used as the input and the aeroelastic forces were outputs. With a hybrid loss function utilizing the prediction errors of both aeroelastic forces simulated by an LSTM network and bridge deck responses calculated by the Newmark-\(\beta\) algorithm to improve the network performance, the LSTM network was trained to be a nonlinear aeroelastic force model for the bridge decks with various leading edges. The simulation results demonstrate that the nonlinear post-flutter behavior of bridges can be accurately and efficiently predicted through the developed LSTM network. Due to the low computational cost, the obtained model can facilitate the aerodynamic optimization of the bridge decks under winds.
# Space Node Topology Optimization Design Considering Anisotropy of Additive Manufacturing
## 3 Results  
### Optimization of Space Nodes  
To highlight the stress complexity of the optimized nodes and to show the robustness of the secondary development algorithm, this paper chose to use ideal cube nodes [17; 18; 31] under combined tension and shear load as a calculation example. The size of the cube design domain was 80 mm \(\times\) 80 mm \(\times\) 80 mm. As shown in Figure 6, one end of the node was fixed, and the other three ends were subjected to axial tension and vertical shear. The shear force was 10,000 N, and the axial force was applied using a pressure of 500 Pa.  
Figure 5: Schematic diagram for adding properties of anisotropic materials.  
Figure 6: Initial shape and load layout of nodes (the green part is the design domain, the gray part is the ear plate area, the yellow arrow is the combined load of axial force and pressure, and the blue area is the binding boundary).  
The sample was defined as an isotropic material with an elastic modulus of 2.06 \(\times\) 105 MPa and a density of 7.85 g/cm\({}^{3}\). The calculation results are shown in Figure 7. With further iterations, the target volume was reached at the 12 th step, and the stress after the final convergence is shown below, highlighting its overall uniform distribution. As a result, there were fewer low-stress areas, with an overall state of high stress.  
Therefore, the BESO secondary development procedure was reasonably reliable.