esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The conventional seismic inversion approach is practical for operational work, as it only uses simple linearized algorithms and assumptions, but may be less applicable when dealing with a complex geological setting, especially in the Malay basin fields, as it may introduce non-linear noises and non-unique solutions. In the Malay basin, we also frequently struggle with a scarcity of reliable well data when performing seismic inversions. This makes finding an accurate prior model for inversion challenging and contributes to high uncertainty in properties' estimation. Implementation of deep learning for seismic inversion has become routine and has shown increasing capability in addressing nonlinearity in inverse problems. In this work, we develop a robust approach to deep learning-based seismic inversion to predict elastic properties from seismic data. The approach incorporates synthetic well and seismic data generation from a set of rock physics knowledge called the rock physics library, which plays a significant role in dataset input for network training, validation, and testing to improve elastic properties in this field. The deep learning network architecture comprising UNET and RESNET-18 with weak supervision networks has proven to be useful to enhance computational work efficiency and prediction accuracy while handling the non-linearity of the data and the non-uniqueness of the solutions. We successfully validated the proposed method on actual field data from a elastic fluvial-dominated field in the Malay basin. Upon comparative analysis with the conventional method, both inversion results are comparable and capable of identifying the reservoir occurrence and distribution. The conventional method exposed the presence of scattered amplitude noises and prominent seismic imprints masking the reservoir. Meanwhile, the proposed method showed more stable, clearer definition and fewer noise inversion results but with a faster turn-around time and a more efficient workflow. There are substantial improvements of up to 31% in correlation accuracy achieved upon implementing the proposed method for elastic properties prediction compared to the conventional. The result implies that the proposed method can provide a good elastic properties prediction framework while addressing data limitations and sparsity issues in typical deep learning-based inversions.  
seismic inversion; weak-supervision deep learning; rock physics 2023
# Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse
## 2 Materials and Methods
### Field Tests  
The WSN network was deployed in the greenhouse following the distribution of Figure 4A,B. The mast that supports each node has a base of 17 kg to give stability and avoid possible rocking. The sink node (red color) and sensor (light blue) were initially in positions A1 and B1 respectively at the same height. The data sink with RSSI information in dBm were sent every 10 s for 10 min, arrived at the sink node. After completing the RSSI data collection, these were moved 2 m to the right (A2, A3, A4 and B2, B3, B4), and the average in the four positions was recorded. This process was repeated again at a different height. Later the sensor node moved from position Bx to Cx and so on as long as there was connectivity with the sink Ax located at the end of the greenhouse lacking line of sight. Finally, we elaborated the measurement curve and contrasted it with the propagation models of radio waves in the presence of vegetation.  
In the literature reviewed, transceiver transmission power and antenna gains were assumed to be those in the technical sheets, and these data were used to calculate the propagation loss later. However, these were far from real and altered the accuracy of subsequent estimates. For this reason, through field tests, the real value of the transmit power was measured so that the results were reliable.  
Figure 3: Internal view of the tomato greenhouse in Almeria. (**A**) Sensor node, (**B**) aisle dimensions, (**C**) distance between floors, (**D**) separation of paired lines, (**E**) main hallway measurement.
## 3 Results  
Table 1 shows the differences in pre and post-oclusal splint intervention in ABFG, SBFG, ABG, SBG, and HG in JFLS-8, GAD-7, OHIP-14, WHO-5, and PSQI. The intervention had a significant effect on all variables in the ABFG. The mean score significantly decreased in JFLS-8 (\(p<\) 0.001, ES = 1.54), GAD-7 (\(p<\) 0.001, ES = 1.69), OHIP-14 (\(p<\) 0.001, ES = 3.52), and PSQI (\(p<\) 0.001, ES = 2.01) in addition to increasing scores for WHO-5 (\(p\) < 0.001, ES = 1.15). When it comes to the SBFG, it also showed significant changes in JFLS-8 (\(p\) < 0.001, ES = 1.71), GAD-7 (\(p\) < 0.001, ES = 1.69), OHIP-14 (\(p\) < 0.001, ES = 2.61), PSQI (\(p\) < 0.001, ES = 2.18), and WHO-5 (\(p\) < 0.001, ES = 1.10). Regarding the ABG, it also showed significant changes in JFLS-8 (\(p\) < 0.001, ES = 0.98), OHIP-14 (\(p\) = 0.02, ES = 0.51), and WHO-5 (\(p\) = 0.01, ES = 0.35). However, no significant changes were observed in GAD-7 (\(p\) = 0.21, ES = 0.21) or PSQI (\(p\) = 0.17, ES = 0.16). When it comes to the SBG, it also showed significant changes in JFLS-8 (\(p\) < 0.001, ES = 1.04), GAD-7 (\(p\) < 0.001, ES = 0.62), OHIP-14 (\(p\) < 0.001, ES = 1.15), and PSQI (\(p\) < 0.001, ES = 0.36). On the other hand, no significant changes were observed in WHO-5 (\(p\) = 0.59, ES = 0.06). For HG, significant changes were only observed in PSQI (\(p\) = 0.01, ES = 0.25). There were no significant differences in JFLS-8 (\(p\) = 0.07, ES = 0.16), GAD-7 (\(p\) = 0.52, ES = 0.06), OHIP-14 (\(p\) = 0.22, ES = 0.17), or WHO-5 (\(p\) = 0.07, ES = 0.25). Table 2 shows the prevalence (%) of temporomandibular disorders pre- and post-occlusal split intervention in ABFG, SBFG, ABG, SBG, and HG. A significant reduction in the prevalence of TMJ pain (\(p\) < 0.001), headache attributed to TMJ (\(p\) < 0.001), and jaw locking (\(p\) < 0.001) was found in ABFG. However, no significant changes were observed in joint sounds (\(p\) = 0.63) and headache attributed to bruxism (\(p\) = 1.00). When it comes to SBFG, significant differences were observed in the following variables: TMJ pain (\(p\) < 0.001), headache attributed to TMJ (\(p\) < 0.001), and jaw locking (\(p\) < 0.001). On the other hand, no significant changes have been observed in joint sounds
# A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic
## 5 Performance Evaluation and Results
### Performance with Different Network Size
This outcome, as expected, highlights the importance of efficient queue utilization in the route selection process. The effect of metric combination based parent selection on the end-to-end delay is lower than the single metric based approach. The OF0 has the longest delay and largest delay variation in comparison to others with increasing network size.
# Ammonia-Oxidizing Bacterial Communities in Tilapia Pond Systems and the Influencing Factors
## 2 Materials and Methods  
### Sample Collection  
Three target tilapia ponds for sample collection were situated at an aquaculture farm located in Yixing (31\({}^{\circ}\)27\({}^{\prime}\)44.2\({}^{\prime}\) N 119\({}^{\circ}\)51\({}^{\prime}\)2.6\({}^{\prime}\) E) of Jiangsu, China. The area of each pond was approximately equal to one another, at 1330 m\({}^{2}\). Tilapia (genetically improved farmed tilapia--GIFT) (_Oroochromis niloticus_) were farmed from fry between 23 May and 15 October 2014. The stocking densities (STD) were 2400 fish/pond (pond 1, the low pond), 3000 fish/pond (pond 2, the medium pond), and 3600 fish/pond (pond 3, the high pond), respectively. The fish were fed after the first month with given formula feed (widely used in China, the composition is as follows: crude protein \(\geq\) 26%, crude fat \(\geq\) 4%, crude fibre \(\geq\) 15%, crude ash \(\leq\) 18%, water content \(\leq\) 12%, total phosphorus \(\geq\) 1%, and lysine \(\geq\) 1.2%). Further, the feeding frequency was four times a day (about 08:00 a.m., 11:00 a.m., 14:00 p.m., and 16:00 p.m.). During the whole period of production, there were no changes in the water and no use of fishery drugs. At the end of the farming, the feed coefficients (FC) were 1.51 (the low pond), 1.21 (the medium pond), and 1.26 (the high pond) for each of the three ponds, respectively. The calculation of the feed coefficient was performed using Formula (1).  
Feed coefficient = the whole weight of bait put in the pond/the whole weight of the fish harvested from the pond (1)  
Samples from the water and sediment were collected from the three ponds on 30 May, 29 July, 26 September, and 10 October 2014. The tools for collecting the water and sediment samples were plexiglass water samples and a gravity-type cylindrical sampler, respectively. After collection, the tubes containing sediment samples were transported in liquid nitrogen and then stored at \(-80\)\({}^{\circ}\)C until further analysis. A total of 200 mL of water was filtered with a hydrophilic polyethersulphone membrane (0.22 \(\upmu\)m for pore size, Jinteng Experimental Equipment Co., Ltd., Tianjin, China) to obtain the target microbes for further analysis. Finally, 12 sediment samples per pond were obtained. The samples from the low pond collected in May were named MS11, MS12, and MS13, respectively, where MS represents May sediment samples, 1 represents pond 1 (the low pond), and the last number represents the three iterations, respectively. Similar naming rules were used for the July sediment samples (JS), September samples (SS), and October samples (OS). The other ponds were also labelled using this format. The water samples were named with the same scheme.
## 6 Case Analysis  
### Introduction to Study System  
Case 1. Single-Hydropower and Single-PV System  
Firstly, an island system with a PV generator and a hydropower generator connected to the grid was selected for modeling analysis. The capacity of the hydropower generator is 140 MW, the terminal voltage is 10 kV, the capacity of the PV generator is 10 MW, and the terminal voltage is 0.4 kV. The two generators pass through a step-up transformer connected to a 110 kV line. The local load is 60 MW + 10 MVar, and the parameters of each component of the hydropower generator are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(K_{P}\) & \(K_{I}\) & \(b_{p}\) & \(T_{W}\) & \(K_{a}\) & \(T_{a}\) \\ \hline
3 & 1.8 & 0.05 & 1 & 400 & 0.055 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of each control link of the hydropower generator.  
Figure 11: Unified frequency model with uncertainty.  
The PV generator adopts constant power outer loop control, and the filter inductance is 0.04 mH. The parameters of the power outer loop and current inner loop control controller are shown in Table 2.  
Case 2. Actual System in a County in Sichuan Province, China  
The site is located on a plateau in the west of Sichuan Province, China. The local capacity to absorb clean energy is relatively poor. When disasters, maintenance issues, or failures occur, the power grid in this area is switched to off-grid operation. The low-frequency oscillation phenomenon in this power grid structure is shown in Figure 12.  
The system includes five hydropower stations, CCB, MP, YJW, HJQ, and MGQ, and two PV stations, XNH and MX. The terminal voltage of the hydropower generators in the system of the demonstration area is 10 kV, and the overall installed capacity is 380 MW, of which that of CCB is 65 MW, that of MP is 55 MW, that of YJW is 75 MW, that of HJQ is 140 MW, and that of MGQ is 45 MW. The terminal voltage of the PV generators is 0.4 kV, the overall installed capacity is 20 MW, and the capacity of XNH and MX is 10 MW in each. The local load is 60 MW + 10 Mvar, and the constant impedance model is used for modeling.  
The outputs of the XNH and MX PV stations are 10 MW and 5 MW, respectively. The hydroelectric units adopt a PID governor. Relevant parameters were designed in accordance with the actual data. The specific parameters are shown in Table 3.
# Automated Tunnel Point Cloud Segmentation and Extraction Method
## 3 Engineering Application
### Analysis of Tunnel Convergence Deformation and Misalignment Deformation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ring Number**} & \multirow{2}{*}{**Standard Data**} & **Centerline** & **Ring Plane** & **Error in Centerline** & \multirow{2}{*}{**Error in Ring Plane**} \\  & & **Extraction** & **Fitting** & **Extraction** & & \\  & & **Technology (m)** & **Technology (m)** & **Technology (mm)** & **Fitting Technology (mm)** \\ \hline KII00 + 549.45 & 6.5050 & 6.5065 & 6.5058 & \(-1.5\) & \(-0.8\) \\ KII00 + 550.652 & 6.5090 & 6.5144 & 6.5104 & \(-5.4\) & \(-1.4\) \\ KII00 + 551.853 & 6.5083 & 6.5097 & 6.5073 & \(-1.4\) & \(+1.0\) \\ KII00 + 552.892 & 6.5113 & 6.5112 & 6.5115 & \(+0.1\) & \(-0.2\) \\ KII00 + 554.255 & 6.5086 & 6.5095 & 6.5090 & \(-0.9\) & \(-0.4\) \\ KII01 + 998.767 & 6.5059 & 6.5067 & 6.5054 & \(-0.8\) & \(+0.5\) \\  & & & & & \\ KII01 + 999.968 & 6.5037 & 6.5046 & 6.5043 & \(-0.9\) & \(-0.6\) \\ KII02 + 001.169 & 6.5049 & 6.5054 & 6.5054 & \(-0.5\) & \(-0.5\) \\ KII02 + 002.370 & 6.5063 & 6.5053 & 6.5069 & \(+1.0\) & \(-0.6\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Analysis of tunnel cross-section convergence deformation and errors between rings compared to field measurement data. Figure 19: Misalignment analysis. process was carefully calibrated and repeated to ensure accuracy. The final measurement results were compared with the total station standard to ensure data accuracy. Based on the segmentation results of a specific ring tunnel, further misalignment analysis can be performed. By extracting the boundary point cloud data of each lining segment after segmentation, and calculating the deviation between the centroid and the overall or theoretical design radius, the full-scale deformation of the tunnel cross-section can be obtained. The specific results of the misalignment analysis are shown in the figure below.
## 2 Model and Mesh Generation
### Grid Division and Monitoring Point Setting  
ANSYS ICEM (ANSYS 18.0 PA USA) as used to draw the grid for the inlet and outlet pipe and cavity connection structure to ensure the accuracy and good convergence of the numerical simulation. Turbo-Grid (ANSYS 18.0 PA USA) was used for the grid division of the impeller and the diffuser. A structured hexahedral grid was used for each component. Then, mesh densification was carried out on the parts near the wall surface and the excessive curvature in the main flow components. The grid of a single channel was established for the impeller and the diffuser. The grid was generated via the periodic rotation around the Z-axis. The structured grid of the pump impeller and diffuser is shown in Figure 2, among which, the O-block topological structure was used for refinement to achieve an efficient simulation.  
Figure 3 shows the setting positions of the pressure data acquisition point in an unsteady numerical simulation of the multiphase mixed-transportation pump. Five pressure monitoring points (IMS1-IMS5) were set on the pressure side of the impeller blade. Five pressure monitoring points (DPS1-DPS5) were set on the pressure side of the diffuser guide vane.
## 5 Experiments and Results
### Experiment 5: Execution Time  
We compared the running time of our approach with the state-of-the-art on four datasets, namely: (i) State Farm Distracted Driver Detection, (ii) Boston University, (iii) DrivFace, and (iv) FT-UMT. It can be observed from Table 4 that ReSVM is 120, 10, 26, 49 times faster than VGG-19, Mobile-Net, InceptionV3, and Xception, respectively, on SFDDD while it is [141; 11; 30; 57] times faster in the case of the BU dataset. A similar trend is observed in DrivFace and FT-UMT datasets. The best execution time was achieved by ResNet-50 but at the cost of reduced accuracy. Our proposed approach, ReSVM, uses similar deep features as that of ResNet-50 but it differs in the use of the classifier. It can be observed from Table 2 that the usage of this SVM classifier increases its percentage accuracy from \(\{89,87.15,87.61,82.50\}\) in the case of ResNet-50 to \(\{95.5,90.46,93.44,94.5\}\) on SFDDD, BU, DrivFace, and FT-UMT, respectively, however, the execution time increases as well.
## 4 Simulation Implementation
### Smart Office Building Floor Plan
#### 4.2.3 Network Bandwidth Usage  
The measurement of network bandwidth usage in iFogSim2 represents the total data transmitted across the network when a request is initiated between the device starting point and the edge device, Fog nodes, and Cloud data centre, and back to the originating device. In large networks, because this comprises a large volume of homogeneous and heterogeneous devices, there is a high potential for applications to experience an increased average of control loop delays, network bottlenecks, and congestion, including interruptions to various services [55]. Compared to Cloud operations, there can be an improved reduction in latency should there be effective balance and management of workload processing at the network's edge and Fog nodes. In addition, data pre-processing at the network's edge and Fog nodes could also decrease the volume of data transmitted across the network. These approaches can improve data flow across the network, reducing bottlenecks, congestion, service interruptions, and CAL delays [55].  
Network bandwidth usage is typically measured in megabits per second in the iFogSim2 toolkit. However, this study presents the network bandwidth usage in Gigabits per second for better visibility. This represents the amount of data that is transmitted over the network. The implications of network bandwidth usage in the fixed scenario are that bandwidth consumption might be lower; however, bandwidth efficiency may be different. In the scalable scenario, it is essential that network bandwidth usage is efficient since the smart IoT devices will be scaled up, leading to network bandwidth usage congestion. This can be addressed by using Fog computing for decentralised computation and data processing closer to the network's edge.  
On the other hand, it is essential to reduce network bandwidth usage as there are multiple endpoint connections in a network that also require large multiple databases for their operation. This also clearly shows that network bandwidth usage mainly depends on network latency and the size of the data tuples, as shown in Equation (5). NU relies on the number of VMs in the Fog nodes assigned for processing and computation [55].  
\[NUI=\sum_{n}l*TNS \tag{5}\]  
_NUI_--represents total network bandwidth usage;  
_l_--represents the latency in the network;  
_TNS_--represents the number of tuples processed simultaneously in the network.
## 3 Mobile Mapping Systems
### Alternative MMS Platforms  
Beyond vehicle-borne systems, MMS platforms vary significantly in design and application. UAV-mounted LiDAR excels in aerial surveys and precision agriculture due to its flexibility and nadir perspective, though it faces payload and endurance limitations. Shoulder-borne and handheld SLAM systems (e.g., GeoSLAM or iPhone LiDAR) enable rapid indoor mapping or forestry surveys but trade absolute accuracy for portability. While these platforms share core technologies (e.g., LiDAR-camera fusion, SLAM), their operational constraints--such as power budgets, kinematic stability, and spatial coverage--warrant distinct calibration and processing approaches. For brevity, this review emphasizes vehicle-borne systems as a benchmark, though many principles (e.g., Section 2's sensor calibrations) are transferable.
# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
## 5 Discussion
### Robustness  
In terms of noise and missing values, the RFC demonstrated greater robustness. By integrating multiple decision trees, the RFC was able to tolerate more noise and interference. On the other hand, the SVC, while capable of finding the optimal hyperplane in high-dimensional feature space, showed weaker robustness in the presence of noise.  
The experimental results showed that, under different RPM and charge conditions, the classification accuracy of the SVC significantly decreased, from 98.9% to 85.3%, as the noise levels increased. In contrast, the RFC maintained higher accuracy under the same conditions, dropping from 99.2% to 94.8%, indicating its stronger tolerance of noise.
# A New Method of Angle Measurement Error Analysis of Rotary Encoders
## 4 Detection and Verification Method
### Installation Eccentricity Detection
#### 4.2.2 Image Processing Technology  
Detailed information about the image processing technology used to get the coordinate of the grating geometric center and the radius of the fitting circle of the rotary scales edge is introduced in this section.  
For each image, the true-color image is converted to a greyscale image, the grayscale histogram of grayscale image is extracted, and then the greyscale image is converted to a binary image, because the binary image can be more convenient for edge extraction and morphological operation. The value of the binary image is reversed for convenience of subsequent calculations. The binary image is separated into several parts, and each part is composed of pixels whose value are \"1\". The threshold value is set to remove the small parts that are caused by noise and dust. The remaining parts are composed of rotary scales. Then the middle point coordinate of the right edge of each part is calculated. The images of rotary scales are shown in Figure 11.  
The circle is fitted by using the least square method. The coordinate of the center of the fitting circle of the rotary scales edge, which is also the center of the grating disk \(O_{p_{k}}(x_{k},y_{k})\) and radius of the fitting circle of the rotary scales edge \(r_{p_{r}fitting}\) are calculated. The original picture size is 2048*1536, and the coordinate of the reference point is \(R(1024,768)\), so then \(l_{p_{r}k}\) and \(d_{p_{r}refk}\) can be calculated, \(k=1,2\cdots 60\).  
Because the pixel value is small, it is hard to calibrate with a conventional checkerboard. Grating period is 20 \(\upmu\)m is known from the datasheet, the number of pixels of three grating periods near the edge along the Y direction is counted, which named \(m_{p}\), \(m_{p}=90\), the distance of each pixel is 0.667 \(\upmu\)m, so the distance between the rotary scales edge and reference point \(d_{refk}\) is represented by Equation (13):  
\[d_{refk}=0.667\times d_{p_{r}refk}. \tag{13}\]  
Figure 11: The cropped images of rotary scales: (**a**) true-color image; (**b**) binary image; and (**c**) middle point of right edge.