esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Verification Examples
### Optimization Design Implementation  
The proposed ABAQUS-MATLAB cooperative framework is applied to optimize the adapter support. To prevent the removal of critical assembly regions during optimization, the support is partitioned into design and non-design domains, as shown in Figure 17a.  
Figure 16: FEA results of the initial adapter support structure. (**a**) Displacement nephogram; (**b**) stress nephogram.  
Figure 15: The initial structural design of the adapter support.  
White regions (non-design domains) interface with adjacent components, while black regions (design domains) undergo optimization. A tie constraint connects these domains in ABAQUS. The CAE model parameters and MATLAB settings are listed in Table 6, with meshing details shown in Figure 17b.  
The use of varying filter radii typically stems from differences in element sizes during mesh generation. In the BESO method, the filter radius should be set to 2-3 times the element size. Generally, to facilitate structural manufacturability and avoid generating excessively fine structural features in the optimization results, the filter radius should be appropriately increased. Under multidimensional constraints (e.g., performance, cost, manufacturability), contextually optimal solutions can be systematically selected rather than passively accepting a single theoretical optimal design.  
The iterative optimization process is illustrated in Figure 18a-d, with the final configuration after 38 iterations featuring curved stiffening ribs and honeycomb-like voids, as shown in Figure 18d. The compliance and volume fraction evolution histories are shown in Figure 19.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Mesh Type**} & **Design** & **Design** & **Elastic** & **Poisson’s** & **Evolutionary** & **Penalization** & **Filter** & **Radius (mm)** & **Volume** \\  & **Domain** & **Demain** & **Element** & **Modulus** & **Ratio** & **Rate** & **Factor** & **Radius (mm)** & **Fraction** \\  & **Size (mm)** & **Count** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** \\ \hline C3D8 & 18 & 24,016 & 130,000 & 0.25 & 4\% & 3 & 50 & 30\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimization design parameters of the adapter support.  
Figure 17: Design domain partitioning and meshing of the adapter support. (**a**) Design domain partition; (**b**) model meshing.  
Figure 18: Iterative optimization process of the adapter support. (**a**) Iteration 8; (**b**) Iteration 16; (**c**) Iteration 25; (**d**) Iteration 38.  
Based on the optimized topology, the redesigned structure (Figure 20) achieves a volume of 0.1287 m\({}^{3}\) and a mass of 940 kg, representing a 31% reduction. Post-optimization FEA results (Figure 21a,b) show a maximum displacement of 0.874 mm and maximum stress of 154.6 MPa, compliant with ASME B30.7-2021 standards [41].  
Figure 21: FEA results of the optimized adapter support. (**a**) Displacement nephogram; (**b**) stress nephogram.  
Figure 19: Compliance and volume fraction evolution histories of the adapter support.
## 3 Results and Discussion
### Influence of the Mixed Eccentricity on the Air-Gap Magnetic Field
#### 3.2.1 Analysis of the Radial Component of the Air-Gap Flux Density  
The radial un-symmetry (_USYr_) and the radial total harmonic distortion rate (_THDr_) are designed for the radial component of the air-gap flux density, which can be described as  
\[USYr=\frac{B_{rm max}-B_{rm}}{B_{m}}\times 100\% \tag{30}\]  
\[THDr=\sqrt{\sum_{k}^{\infty}\biggl{[}\frac{B_{r}(k)}{B_{r1}}\biggr{]}^{2}} \times 100\% \tag{31}\]  
where \(B_{r}\) is the spatial distribution of radial component of air-gap flux density, and \(B_{rm}\) and \(B_{rm}\) are the maximum and minimum root mean square values of all periods of \(B_{r}\). \(B_{m}\) is the root mean square value without eccentricity. \(B_{r1}\) is the amplitude of fundamental component of the radial air-gap flux density, and \(B_{r}\)(\(k\)) is the amplitude of \(k\)th harmonic component of radial air-gap flux density.  
In order to study the influence of different degrees of static and dynamic eccentricities on radial air-gap flux density, the results of two situations are shown in Table 3; \(\varepsilon_{S}=20\%\) and \(\mathrm{t}\varepsilon_{S}=50\%\) for the first situation, while \(\varepsilon_{D}=50\%\) and \(\varepsilon_{D}=20\%\) for the second situation. From the table, we can find that the spatial distribution of the air-gap radial flux density is distorted by mixed eccentricity. Compared with the case without mixed eccentricity, the values of _USYr_ and _THDr_ increase when the mixed eccentricity occurs. In the situation of \(\varepsilon_{S}=50\%\) and \(\varepsilon_{D}=20\%\), the _USYr_ increases by 0.19%, the amplitude of spatial harmonics increases by 0.09% averagely, and the _THDr_ also increases by 0.02%. It can be seen that the influence of static eccentricity of the rotor on air-gap radial flux density is greater than that of dynamic eccentricity.  
In order to further highlight the influence of mixed eccentricity on the features of radial air-gap flux density, the degrees and steps of static and dynamic eccentricities were set to be the same, so the degree of mixed eccentricity is the same as the degree of static eccentricity or dynamic eccentricity for the analysis of the variation characteristic of Br with a mixed-eccentric fault. The air-gap flux density was calculated when the static eccentricity angle was \(2^{\circ}\), and the result is shown in Figure 7. The values of _USYr_ and _THDr_ of the radial air-gap flux density also increase with the increase in the degree of mixed eccentricity. When the degree of the mixed eccentricity is low (\(\varepsilon_{m}<40\%\)), the values of _USYr_ are low and change smoothly, while when the \(\varepsilon_{m}>40\%\), the values of _USYr_ increase significantly.
## 1 Introduction
Visual filtering approached for event logs that make the process analysis tasks more feasible and tractable are conducted and analyzed with the event log data from Business Process Intelligence (BPI) challenge 2017 [10]. Although the idea of creating a CPN model based on process mining is promising, the first practical implementation still uses general/incorrect assumptions--for instance, all the resources are available if no task is being performed, and the processing time of the resources when executing tasks is always based on a specific underlying distribution in any condition [4]. Several studies have attempted to mitigate this limitation. In particular, the resource behavior in process mining has been analyzed to explore the effect of workload on service times and subsequently incorporate it into the simulation model [11]. This study assumed that a worker under time pressure may become more efficient and can consequently finish tasks faster [11]. Another study calculated the batch process and resource availability for subsequent incorporation into the CPN model [12]. The event logs were analyzed to calculate the percentage of resource availability in a specific period [12]. Our study attempts to complement the previous studies on resource availability by incorporating shift time. The previous study did not set resource availability at specific times, i.e., it did not consider that resources, particularly humans, generally work based on shift times. Although the simulation model incorporates resource availability based on percentages, it still has a gap from reality, as it does not consider shift times. Without knowing the information related to the shift time, the simulation model generated could still not imitating reality. We attempt to use an event log to analyze and cluster the shift times of resources so that this information can be used in the simulation model and could improve the accuracy of the model. The main contribution of our research is as follow:* We proposed the clustering method of mining the shift work operation of resources that can be put in the simulation model to generate a more accurate model. * We propose the new distance function to handle the problem that can be happened when clustering the time data. * We propose the new representation of activity that can handle the resource shift work rule in CPN. The remainder of this paper is structured as follows. Section 2 discusses some terminology, provides an overview of the related work, and presents a running example. Section 3 outlines the data-driven method to retrieve shift work information. Section 4 will explain about how we incorporate this shift work information in the simulation model. Section 5 describes the explanation about the mechanism of how we generate the artificial event logs. The result from the method in Section 3 is empirically evaluated in Section 6. The paper ends with a discussion in Section 7 and a conclusion in Section 8.
## 4 Communication Standards for Wireless Sensor Networks
### Bluetooth  
Bluetooth is a short distance wireless communication technology based on IEEE 802.15.1 standard. It uses short wavelength wireless transmission in the unlicensed ISM band from 2400 to 2480 MHz. It uses frequency hopping spread spectrum (FHSS) technology and around 1600 hops per second. Its key features are extensive availability, low power consumption and rapid data exchange. Bluetooth was initially developed in 1994 by Ericsson and then a group of firms formed a special interest group to retain and improve this technology. There are two network topologies used in Bluetooth which are termed as Piconet and Scatternet. A Piconet is created by a Personal Area Network in which one wireless client acts as a master and other wireless clients serve as slaves. Maximum eight devices can communicate with each other in one Piconet. A Scatternet is an arrangement of group of Piconets. Bluetooth is used for communications between smart consumer appliances, energy management system and smart meters. It has peak data throughput of 1 Mbps, 79 radio frequency channels and channel bandwidth of 1 MHz. It has nominal range of around 10 m. Bluetooth comprises of three power classes each having a different range [59].
# A Quality Control Method for High Frequency Radar Data Based on Machine Learning Neural Networks
## 3 Results
### Single Anomaly
However, if the model can learn and adapt to simulated anomalous data during the training phase, its ability to recognize anomalies and stability in real scenarios will be significantly improved. For simulating anomalous information, we opted for Gaussian distribution, also termed normal distribution, a common probability distribution in statistics. This distribution simulates the overall effect of multiple small random error factors in the real world. According to the Central Limit Theorem, these effects exhibit a shape that is approximately normally distributed, making Gaussian distribution an ideal choice for our purposes. During the anomaly injection process, we fine-tuned the Gaussian distribution's relevant parameters to ensure that the produced anomalies clustered mainly within a range akin to observed real-world anomalies. Specifically, from 15 different locations, we randomly selected 10% of the data and introduced anomalies at the 't' time point. To emulate various sudden changes potentially present in actual data, we then chose another distinct 10% data subset, introducing anomalies at either the 't \(-\) 1' or 't + 1' time points. Figure 7a,b compares the predictions of the original and improved models at two different time points. As observed from the charts, the improved model can effectively identify anomalous data by producing large residuals. Simultaneously, it excels at making accurate predictions for normal data, with almost negligible residuals. Figure 7c presents the PR curves of both models. As illustrated in the chart, the PR curve of the improved model consistently envelops that of the original model, indicating that, at equivalent recall levels, the improved model achieves greater precision. Figure 7d displays the precision and recall of the new model at different thresholds. Viewing the entire dataset further validates the observations made from Figure 7a,b, where the model significantly reduced residuals for normal data even in the presence of anomalous data. This is reflected in the graph where the model retains high precision even at lower thresholds. All sub-figures in Figure 7 validate the efficacy of our model's improvements. The F-score of the new model has increased from 0.63 to 0.79, indicating, roughly, a 16% improvement over the old model. By injecting simulated anomalous data into the training set,we have not only enhanced the model's generalization capabilities but also its robustness against anomalies.
# A Review of Image Inpainting Methods Based on Deep Learning
## 4 Deep-Learning-Based Image Inpainting Algorithm  
As illustrated in Figure 1, existing deep-learning-based image inpainting methods generally adopt the approach of first compressing the damaged image into a latent space code through an encoder, then recovering the latent space code into the restored image through a decoder.
## 3 Results and Discussion
### PdM Assessment of Case Study
#### 3.1.1 Step 1: System Boundary, Interfaces, Use Case Scenarios, and Architecture  
In the context of the O&G industry, centrifugal compressors are used to enable the transportation of natural gas through subsea pipelines. An electric motor drives the centrifugal compressor by rotating the rotor. During operation, the gas is fed to the rotor and its rotating impellers. When the gas hits the rotating impellers, this accelerates the velocity of the gas and increases its kinetic energy. Next, the high-velocity gas is discharged into the diffusor (gas path with increasing radius), reducing its velocity and converting the recently introduced kinetic energy into static pressure.
# Gear Wear Process Monitoring Using a Sideband Estimator Based on Modulation Signal Bispectrum
## 3 Experiment Setup and Procedure  
To verify the effectiveness of MSB-SE-based gear wear monitoring, vibration signals were acquired from a two-stage gearbox test system under accelerated test conditions. The gearbox under test is rated at 11 kW at 1470 rpm with splash lubrication. The technical specification of the helical gearbox is given in Table 1. Figure 1 shows the position of the accelerometer mounted on the middle of the outer surface of the gearbox casing, where it is closer to the bearings on the middle shaft. In this way, both stages of the gears can be monitored by one sensor. To accelerate gear failure, the tooth width of the gear on the input shaft at the first stage was halved. This increases local contact stress from 727.8 MPa to 878.7 MPa, and it is still less than the contact fatigue strength limit of gear material 16 MnCr5 with surface hardness 59 to 63 HRC, 1450 MPa [25].  
Figure 1: Schematic diagram of two-stage gearbox.  
In the experiment, the gearbox operated at its full speed under load cycle of five load conditions (0%, 40%, 60%, 80%, and 90% of the full load). In each cycle, the duration for the load of 90% was 34 min, and for other load conditions was four minutes individually. The vibration was measured by a general-purpose accelerometer with a sensitivity of 28.7 mv/(ms\({}^{-2}\)) and frequency response ranging from 1 Hz to 10 kHz. All the data were logged simultaneously with length of 30 s for each load case at an interval of 50 min by a multiple-channel high-speed data acquisition system with 96 kHz sampling rate and 16-bit resolution.  
The test was terminated after 574 h (689 load cycles), due to a significantly growth of the vibration level, which indicated that there was a clear abnormality occurring in the system. The gearbox was then dismantled and inspected. It was found that clear tooth defects were present on the gear teeth surface, as illustrated in Figure 2. It shows that the tooth surface of the driving gear had obvious scuffing marks, as illustrated in Figure 2a,b, in which excessive wear effect is more significant on the tooth flank below the pitch line. Moreover, it also shows that the wear marks in Figure 2b look slightly higher, showing non-uniform wear across the circumference of the gear or unsymmetrical wear due to inevitable eccentricity and run-out errors. These phenomena are typical features caused by the excessive wear under higher contact stress, which are common failures and have been previously observed in many studies [26; 27; 28; 29; 30; 31; 32].
## 2 Results and Discussion
### Molecular Docking
Moreover, the hydroxyl group of (S)-(+)-2-amino-1-propanole substituent interacted with the Ni\({}^{2+}\) ions with distances of 2.306 and 2.255 A between the O atom and Ni\({}^{2+}\) ions (Figure 8a). As can be seen in Figure 8b, the molecule of **8i** bound to an active site of urease through N1... C (ALA636A; 3.175 A) but in a different pose than the molecule of **8a** with the pyrazolo[4,3-_e_][1,2,4]triazine ring in the vicinity of Ni\({}^{2+}\) ions. Figure 8: A view of the interaction of (**a**) **8a** and (**b**) **8i** with amino acids of the binding site in jack bean urease. The molecular docking study showed that the compound **8a-j**, showing experimentally confirmed activity in the direction of inhibitory effects on mushroom tyrosinase and jack bean urease, had better affinity to these enzymes than their ligands in a crystalline state. The Cu2+ and Ni2+ ions present in the active site of tyrosinase and urease, respectively, were important centers in the interactions of ligands with enzymes.
## 3 Implementation
### Implementation of Brick Model  
Given our focus on the lighting system, the parts of the case study building that are relevant to our PT include the electrical distribution tree with positions of meters and lamps, and which rooms these lamps located in. More specifically, we need to be able to map a meter to a covered area (the union of all rooms containing lamps measured by the meter) and a maximum wattage (the sum of the maximum wattages of all lamps measured by the meter).  
To support this mapping, we modeled the case study building in a version of Brick what we extended in three ways: (i) the meters were associated with an ID for reference--the same one that they have in the archiver; (ii) the lights we associated with a maximum power; and (iii) the rooms were associated with a surface area. Each of these annotations are encoded as RDF literals. For portability, both the power and the area mention their SI unit explicitly. Figure 7 highlights the basic structure on which queries are resolved. The full graph references 22 m, 929 lights, and 222 rooms.  
The data for populating the model was manually extracted and linked from three sets of technical drawings covering the electrical distribution tree, the lighting system (type and wattage), and base room information (area).  
Figure 7: Substructure of Brick model relevant querying, showing how meters (M), lights (L), and rooms (R) are linked.  
In an effort to verify that the model is both consistent and a good representative of the case study building we have performed a selection of sanity checks. To do this we added room type to the model and verified that area and light power density followed a Gaussian distribution for each room type. We also added room names that contain the floor in the string of their name and used this to verify the room \(\rightarrow\) floor map.
# A Joint Detection and Decoding Scheme for PC-SCMA System Based on Pruning Iteration
###### Abstract  
Polar coding and sparse code multiple access (SCMA) are key technologies for 5G mobile communication, the joint design of them has a great significance to improve the overall performance of the transmitter-receiver symmetric wireless communication system. In this paper, we firstly propose a pruning iterative joint detection and decoding algorithm (PI-JDD) based on the confidence stability of resource nodes. Branches to be updated are dynamically pruned to avoid redundant iterative, which is able to reduce 24-50% complexity while achieving the approximate error performance of traditional serial joint iterative detection and decoding algorithm S-JIDD. Then, to further reduce the bit error rate (BER) of the receiver, a cyclic redundancy check (CRC) termination mechanism is added at the end of each joint iteration to avoid the convergence error caused by decoding deviation. Simulation results show that the addition of an early stopping criterion can achieve a remarkable performance gain compared with the S-JIDD algorithm. More importantly, the combined algorithm of the two proposed schemes can reduce the computational complexity while achieving better error performance.  
sparse code multiple access (SCMA); polar code; joint detection and decoding; pruning iteration; CRC termination mechanism +
Footnote †: journal: Computer Science and Engineering
# On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
## 4 Results and Discussion
### Model Evaluation
#### 4.1.2 Model Sizes  
Table 7 compares the models, the respective quantization technique, and the corresponding sizes in MB. The smallest quantization technique in each model has been highlighted for easier legibility.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Quantization Technique**} \\ \cline{2-4}
**Model** & **F32** & **F16** & **QAT** \\ \hline EfficientFormerV2S2 & 82.59 & 82.60 & 25.11 \\ EfficientViT\_B0 & 90.02 & 90.50 & 15.65 \\ EfficientViT\_M4 & 91.91 & 91.69 & 89.44 \\ MobileViT\_V2\_050 & 90.34 & 90.33 & 70.02 \\ MobileViT\_V2\_100 & 93.93 & 93.93 & 85.64 \\ MobileViT\_V2\_175 & 95.34 & 95.34 & 94.42 \\ RepViT\_M11 & 91.72 & 91.72 & 93.11 \\ \hline \hline \end{tabular}
\end{table}
Table 6: F1-scores for different ViT models and quantization techniques.  
Figure 5: Combined F1-score values for three types of quantization of each ViT model.  
Across all seven models, the quantization technique that reduces the model size the most is the QAT method. Table 8 shows the percentage of the original (F32 model) size that each of the other quantization techniques is scaled down to. Once again, it is evident that QAT performs the best in terms of reducing the scale of the model's size.  
Figure 6 presents a similar visualization to Figure 5, where we can see the drop in the size of the model via bar charts. Here, we use a threshold of 30 MB to indicate a generally \"small\" model.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Quantization Technique**} \\ \cline{2-4}
**Model** & **F32** & **F16** & **QAT** \\ \hline EfficientFormerV2S2 & 48.04 & 24.22 & 13.71 \\ EfficientViT\_B0 & 7.19 & 3.63 & 1.98 \\ EfficientViT\_M4 & 33.81 & 17.01 & 9.09 \\ MobileViT\_V2\_050 & 5.50 & 2.89 & 1.70 \\ MobileViT\_V2\_100 & 18.98 & 9.63 & 5.22 \\ MobileViT\_V2\_175 & 54.66 & 27.47 & 14.36 \\ RepViT\_M11 & 31.72 & 15.96 & 8.46 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Size comparison in MB for different ViT models and quantization techniques.  
Figure 6: Combined size values in MB for three types of quantization of each ViT model.
## 3 Major Agriculture Applications and Services
### Soil Monitoring
Measurement of the flow rate of seed is conducted through signals correlated by transferring seeds. Overall, advanced technologies and tools help analyze soils, which further help monitor crop growth and production.