esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of AC Corrosion Probability in Buried Pipelines with a FEM-Assisted Stochastic Approach
## 1 Introduction
Since AC interference modelling problems are essentially three-dimensional and given the considerable size of corridors, field-theory-based models have mostly been used with circuital approaches. Hence, FEM is used to infer physical information on selected cross sections of the corridors, subsequently used to build an equivalent circuit representation of the whole geometry. The models in [15; 16; 17; 18; 19; 20; 21] are examples of efforts in this direction. These have been applied to study complex non-parallel configurations in conjunction with complex soil models and the presence of multiple conductors. Some work, such as [22] and later on [23], has also shown the advantages of combining FEM (used only for the pipeline region) with the boundary element method (BEM). Notably, several commercial programs feature comprehensive modelling capabilities regarding electrical interference on pipelines (and grounding systems), often allowing one to perform studies using circuit and electromagnetic field theory models. Examples of such computer programs include CDEGS [24], which originated from the seminal modelling works of [PERSON] and colleagues [25], Elysca [26; 27] and XGSLAB [28; 29]. Table 1 summarizes the calculation methodologies employed in the above-discussed works. For each work, the evaluated physical quantities are also reported. A feature shared by most of the listed approaches and corresponding computer codes is that deterministic approaches are applied to estimate the physical quantities of interest. In contrast, this work uses field theory and circuit methods combined with a stochastic approach to study how holidays' geometrical and electrical properties influence the probability of exceeding the current density limit for corrosion. A similar approach has been used in [30] without considering the presence of coating defects, which are the main focus of the present manuscript. In this work, three-dimensional FEM simulations are performed to study the electrical properties of a holiday (pore) in the insulation layer of a metallic pipeline. The focus is set on the influence of the shape and electrical resistivity of the pore on the obtained value of spread resistance. Once the physical characteristics of a typical holiday have been defined, an evaluation of the leakage current density is performed using a probabilistic method. The computed quantity is the probability of exceeding a given current density value. In this way, the influence of both pore and external soil resistivity on exceeding the current density limit is assessed. Finally, the developed probabilistic approach is applied to a more complex case by computing the probability of exceeding the limit along a pipeline transmission line routing. The parametric FEM simulations on the holiday spread resistance are described in Section 2. The probabilistic evaluation of the current density is discussed in Section 3.
## 3 Common-Mode Noise and Signal Processing Method for Reconstruction of PC Display  
### Measurement of Common-Mode Noise  
In a desktop PC, the power cables are connected to the electrical outlets, which may extend outdoors even if the PC stands alone. Thus, we first examined the distribution of the common-mode noise caused by the monitor signal on the PC and the monitor power cables to detect the possibility of remote information reconstruction. The experiments were carried out for both cases, where the power cables were put on the table and on the floor. Each power cable had the length of 1.85 m. A coaxial parallel serial cable with a length of 1.85 m was used for the monitor signal cable.  
Figure 2 shows the level of the common-mode noises depending on the measurement position, which was defined by the distance along with the power cables from the outlet to the current probe used for the measurement. The noise levels were estimated as peak to peak current values measured with the current probe and averaged three times. A simple image in which the left half was the white color and the right half was the black color (shown in Figure 1) was displayed on the monitor with a resolution of 800 x 600 pixels. The noise level was measured at a fundamental frequency of 38 kHz with a spectrum analyzer. The noise levels were too low to observe with the oscilloscope. At the probe positions of more than 1.6 m (near the PC or the monitor), the noise levels became high except for the case where the PC power cable was put on the floor. In the cases where the position of the current probe was less than 1.6 m, the levels exhibited roughly constant values of 20-40 A. The constant noise levels were reasonable if the common-mode noise current was caused by the capacitive coupling between the cables and the earth [17].  
Figure 3 shows the common-mode noise distribution on the monitor signal cable under the same measurement conditions as above. In this case, the position was defined as the distance along the signal cable from the PC to the current probe. In Figure 3a, the levels were measured only on the coaxial part of the serial cable. The levels were constantly independent of the position, and there was  
Figure 2: Dependences of the common-mode noise level on the distance from the outlet of the power cables: (**a**) personal computer (PC) power cable, (**b**) monitor power cable.  
no difference observed between the cases when the signal cable was put on the table and on the floor. The common-mode noise levels measured with only the coaxial cable for a monitor signal cable are shown in Figure 3b for a comparison. The noise levels decreased to about one-third compared with that of the coaxial parallel series cable shown in Figure 3a; this was a result of the noise reduction effect by the coaxial cable.  
From the above experimental results, we can confirm that the common-mode noise, due to monitor signal, conducts on power cables and leaks toward the outdoor. The current level of the noise measured with this system was too low for waveform-observation, so the noise on monitor signal-cable was aimed for reconstruction, as mentioned in the next section.
# Exploring the Role of Distraction in Weak Flow-Performance Link Based on VR Searching Tasks
## 8 Discussion
### Design Guideline and Its Practical Implication  
Based on the principle proposed in WAM [2], we proposed two more specific design guidelines to deal with distractions in VR: (1) Reducing conspicuous but irrelevant distractors directly; and (2) increasing the congruence between distractors and primary task.  
When conspicuous but irrelevant distractors induce distractions during the primary task, directly reducing the number of them is an easy and practical approach. This approach can effectively reduce distraction, improve task-relevant behaviors, and the performance of primary task. Not only that, but it can also strengthen the flow-performance link to a certain extent. However, this approach impairs the quality of flow experience to some extent. Some distractors can improve the vividness and interactivity of the virtual environment. Researchers have agreed that interactivity and vividness are two key variables affecting presence [33; 34; 35; 36], and more vivid and interactive virtual environments are associated with higher levels of telepresence [37; 38; 39; 40]. Therefore, distractors are not totally useless and they may have the potential to enhance the user's feeling of presence and motivation of automatic exploration (autotelic experience) in VR, which helps achieve better flow experience. It may be better to take advantage of these distractors than to remove them entirely. Guideline 2 was proposed in this direction.  
Guideline 2 gives another approach to dealing with distractions during the primary task. It is a constructive way to transfer the consequences of distraction into task-relevant ones through providing appropriate visual cues. In addition to the previous several studies that gave us inspiration, there are also some existing studies that are in line with this direction of design. [PERSON] and [PERSON] [31] used a gaze-contingent search paradigm to manipulate selection history directly and examine the competition between multiple cue-matching saccade target objects. [PERSON] et al. conducted A meta-analysis to explore a concurrent working memory load task that does not impair visual selective attention [41]. These works can also provide inspiration to adjust visual information. Finally, we optimizedthe disjointed VR system to a more congruent one and proved the second guideline is effective and practical too. This approach can help transfer the participants' attention back to the primary task without reducing the number of distractors, and it does help participants improve task-related behavior and performance without damaging the quality of flow. In this way, much flow may be produced from performing the primary task and task-relevant behavior, and a stronger flow-performance link can be built.  
These findings can provide reference for designers to find ways to optimize VR systems. As to how to make the best use of these guidelines, the specific features of each VR system and the type of the primary task need to be fully considered.
# Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks
## 3 Methods
### Energy Harvesting System
#### 3.1.2 Design of Power Supply Circuit  
The power supply circuit is mainly divided into three parts. The first is the solar power generation circuit, as shown in Figure 3A. The solar panel can output about 5 V DC voltage, and the large capacitance is used to maintain the stability of the voltage. The second is the piezoelectric ceramic power generation circuit, as shown in Figure 3B. We use the MP1541 chip to design voltage boost conversion circuit with a fixed frequency and peak current mode. The third is the electric energy storage circuit, as shown in Figure 3C. We use the TP4056 chip to design the charging circuit completing the power supply to the lithium battery.  
Figure 3: _Cont._  
Figure 2: Framework diagram of node energy supply in a WSN.
## 4 Results
### Model Performance on the Test Set  
The model performance on the test set with ResNet50-FPN as the backbone network over different IoUs are shown in Figure 8. When the IoU increases from 0.5 to 0.95 (IoU = [0.5:0.95]), the AP value of the model increases gradually and then tends to be stable with the increase of training iterations; the loss function value first decreases and then tends to be stable, and the model converges normally. When the IoU = 0.5, the trained model has the highest accuracy, the F1-Score of bounding box and mask are both larger than 92%.  
Figure 9 presents the detection and segmentation performance on one test set, the same image as Figure 3 is given as an example. This model can detect and segment the mature green tomato fruit area, especially for those images with fruit overlapping, leaf occlusion, branch occlusion and other complex states of the tomato where traditional image detection algorithms are difficult to solve.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Backbone Network** & **FPS** & **F1-Score\({}_{\text{bbox}}\)** & **F1-Score\({}_{\text{Mask}}\)** & **Index** \\ \hline ResNet50 & 5.77 & 0.9336 & 0.9241 & 0.8531 \\ ResNet50-FPN & 26.10 & 0.9290 & 0.9284 & 0.9430 \\ ResNet101-FPN & 19.53 & 0.9265 & 0.9300 & 0.9135 \\ ResNeXt101-vd-FPN & 9.34 & 0.9302 & 0.9323 & 0.8710 \\ SENet154-vd-FPN & 3.49 & 0.9318 & 0.9345 & 0.8465 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation index values for mature green tomato dataset under different backbone networks.
# Native Smartphone Single- and Dual-Frequency GNSS-PPP/IMU Solution in Real-World Driving Scenarios
## 3 Measurement Campaigns
### Environmental Consideration  
Since this study aims to mimic daily driving, a route including open parking lots with vegetation, suburban roads and mixed underpasses was chosen. Examples of Google Street View photos is shown in Figure 4. Road tests were performed on three separate days using an identical route around York University, Toronto, Canada, and a total five datasets from the two phones were collected. The route takes 26, 16, and 24 min for each road tests over a total driving distance of approximately 10 km.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phone Models** & **Xiaomi MI 8** & **Samsung Galaxy S21+** & **Xsens MTi-7** \\ \hline
**IMU Model** & \multicolumn{2}{c}{InvenSense ICM-20690} & ST-Microelectronics LSM6 DSO & N/A \\ \hline \multicolumn{4}{c}{**Gyroscope**} \\ \hline
**In-run bias stability (\%/h)** & \textgreater{}1000 & - & 10 \\ \hline
**Noise density (\%/\(\sqrt{\text{Hz}}\))** & 0.004 & 0.004 & 0.003 \\ \hline
**Standard full range (\(\pm\)°/s )** & 2000 & 2000 & 2000 \\ \hline \multicolumn{4}{c}{**Accelerometer**} \\ \hline
**In-run bias stability (mg)** & 40 & - & 0.03 \\ \hline
**Noise density (mg/\(\sqrt{\text{Hz}}\))** & 100 & 110 & 70 \\ \hline
**Standard full range (\(\pm\)g)** & 16 & 16 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Factory specifications of the inertial sensors used in experiment [49; 41; 42; 44; 50; 51].  
Figure 4: Google Street View of experimental environments.  
Based on the example collected on 30 November 2021, the effects of outages are briefly discussed with a more detailed quantitative analysis to follow. Outage A is caused by a railway bridge overpass with a width of approximately 6 m over an arterial road. The overpass caused a reduction in the number of visible satellites for over 3 s, approximately 5 min from the start of data collection. Outage B is a more complicated underpass below a major highway and two on/off ramps, consisting of three separate complete outages over 100 m with two small gaps, each approximately 10 m. The duration of Outage B is \(\sim\)10 s at an elapsed time of 15 min from the beginning of the drive. The complexity increases the difficulty in producing a good GNSS solution as momentary GNSS signal re-acquisition produce poor quality measurements and the filter has little time to converge before subsequent outages.
# Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review
## 5 Identified Challenges and Solution Directions
### RQ2. What Are the Identified Solution Directions?
#### 5.2.8 Monitoring, Tracing, and Logging
For this purpose, the authors come up with a method for detecting microservice failures by using a semi-supervised learning model and dynamic sliding window methods. To evaluate their model, they used public data and the results showed that the model had a good performance and the accuracy of anomaly detection and root cause location was close to 100%. Study [BU] addresses availability issues caused by service anomalies. The authors stated that existing approaches were limited in terms of inefficient traversing mechanism of service dependency graph and detecting anomalies process also could result in failure. To this end, they propose a highly efficient root cause localization approach based on dynamically constructed service call graphs. Experimental results and the result of being used in Alibaba showed it obtained good results in terms of accuracy and efficiency. Study [BV] aims to address the root cause of performance issues. The authors claim that complex communication among services makes the system performance unpredictable and hard to trace and detect the root cause of performance issues. Therefore, they propose a tool called T-Rank. It uses tracking data and combines them with a tracing chain. Further, it provides a ranked suspicious list of the containers based on the spectrum algorithm. As a result of their experiment with the data collected from a real-world MSA-based system, T-Rank is feasible to be used in MSA base system thanks to its high accuracy and low resource cost. The authors [CE] propose an anomaly detection approach for MSA-based systems. They stated that existing approaches do not have the required skills to detect faulty services accurately. Therefore, they propose an anomaly detection approach. In this approach, first, execution traces are collected across microservices, then the anomaly degree of traces is calculated and then differences between traces are analyzed to locate the components causing anomalies. According to their evaluation results, this approach achieves high precision and recall in detecting anomalies. Study [CG] provides an agent-based monitoring platform by monitoring not only internally developed services but also externally developed services with the help of sidecar containers. Agents are responsible for monitoring incoming and outgoing network traffic and also system state by reading kernel data. Prototype evaluation results show that their solution has a similar performance as Prometheus, but also, they offer some functionalities focused on multi-vendor service integration. In summary, it is important to monitor the environment after developing microservices, so these studies in this part generally focus on the monitoring architecture model, extract dependencies, and anomaly detection. There is a lack of a powerful tool with integration API with other third-party software among these studies.
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 4 Discussion
A possible explanation is that ANN is very sensitive to the quality of features and has the trend of over-training when dealing with features with disturbance. Therefore, in compact and dual-polarimetric SAR modes, ML performs better than ANN, although the latter one is more sophisticated in its architecture. This study shows that polarimetric SAR can distinguish mineral oil from biogenic slicks. An important result is that the identification of different oils (bunker oil, crude oil, petrochemical films) is very important for clean-up operations. Different oils have different physical/chemical properties, e.g., viscosity, density, evaporation rate, etc., and theoretically, a difference in these properties can be observed in polarimetric SAR images. However, currently, there is not enough valid data to support this latter postulate. This analysis can be made in the future. It is important to analyze the behavior of weathering oil in polarimetric SAR images. Particularly, evaporation, emulsification and sinking are important related slick detections by SAR. Studies [40; 41] indicate that the percentages of oil trapped, evaporated and at the surface vary with the type of oil spilt and with the location in which spills are firstly generated. In essence, the movement of oil, its original type/density and the time that leads to its emulsification/evaporation/sinking are variable in different oil spills. It is also considered crucial to understand the effects of emulsification and ocean-driven slick movement in the size(s) and distribution of oil slicks at the surface for environmental protection [42]. Hence, more detailed experiments should be made to quantitatively analyze the degree of degradation of an oil spill based on polarimetric SAR.
## 2 Materials and Methods
### Experimental Setup  
Printing operations were conducted on a low-cost ANYCUBIC photon Mono X resin 3D printer; this is shown in Figure 1, and its specifications are shown in Table 5. New water-washable resin from Creality was used as a UV-sensitive material for the 3D printer. A cube with a dimension of 10 mm and a dental bridge from Vital were designed in SOLIDWORKS and EXOCAD, respectively, and downloaded as STL files to the printer software's (Photon Workshop V2.1.21.RC6) cube slicer. The software slices the cube in layers, and the cube file is downloaded to the Mono X resin 3D printer via a flash disk. Firstly, instead of printing the complicated shapes of nine dental bridge models, nine cubes as samples with different parameters for each experiment were printed. Secondly, the same above experimental procedure was repeated for printing the dental bridge using the optimum values of the parameters obtained from simple CAD parts (cubes). When the bottom exposure period gets longer, the raft becomes more securely attached to the build plate, leading to a longer printing time [27]. The room temperature for all nine experimental conditions was 26-27 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Run Order**} & \multicolumn{2}{c}{**A**} & \multicolumn{2}{c}{**B**} & \multicolumn{2}{c}{**C**} \\  & **Coded** & **Actual** & **Coded** & **Actual** & **Coded** & **Actual** \\ \hline
1 & 1 & 1.2 & 1 & 15 & 1 & 1 \\
2 & 1 & 1.2 & 2 & 35 & 2 & 4 \\
3 & 1 & 1.2 & 3 & 55 & 3 & 7 \\
4 & 2 & 1.85 & 1 & 15 & 2 & 4 \\
5 & 2 & 1.85 & 2 & 35 & 3 & 7 \\
6 & 2 & 1.85 & 3 & 55 & 1 & 1 \\
7 & 3 & 2.5 & 1 & 15 & 3 & 7 \\
8 & 3 & 2.5 & 2 & 35 & 1 & 1 \\
9 & 3 & 2.5 & 3 & 55 & 2 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Taguchi level design for input parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input Parameters** & **Units** & \multicolumn{2}{c}{**Coded Levels**} & \multicolumn{2}{c}{**Actual Values**} \\ \hline Normal exposure time & Seconds & 1 & 2 & 3 & 1.2 & 1.85 & 2.5 \\ Bottom exposure time & Seconds & 1 & 2 & 3 & 15 & 35 & 55 \\ Bottom layers & Count & 1 & 2 & 3 & 1 & 4 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Printing parameters and their levels.  
The layer thickness for all experiments was 0.050 mm. It has been reported in the literature that when using SLA technology to print samples with a decrease in layer thickness, the strength and accuracy of the sample increase.
# Real-Time Feedback in Swimming Exercises Using Embedded Devices in Water
## 2 Materials and Methods
### Vote Request
Following data acquisition, the Dynamic Time Warping (DTW) algorithm is executed locally on each slave node to compute the cost matrices and similarity score, a process that requires approximately 8.5 ms. Each slave node independently performs its DTW computation and provides immediate haptic feedback to the user via a vibration motor upon completing the similarity analysis. Simultaneously, the computed results are transmitted to a master node over Wi-Fi. This transmission is conducted in parallel and is intended primarily for debugging and visualization purposes. The inter-device communication, implemented using the ESP-NOW protocol, introduces a transmission delay of less than 50 ms. The master node subsequently formats the received data as JSON and pushes it to a client browser using WebSocket technology. This enables near real-time display, with client-side parsing and rendering contributing negligible additional delay. The overall system architecture ensures timely feedback for the user while supporting efficient remote monitoring. In this configuration, the total time elapsed from the initiation of the movement to its visualization on the display is approximately 2.60 ms.