esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For example, the pore space of the rock was unblocked, and the permeability was restored to 51% [23]. Sonification was allowed to restore from 50% to 75% of the permeability of alloxide discs with paraffin deposits [24]. Microwave and ultrasonic treatments can keep asphaltenes in a dispersed state to prevent deposition on rock walls [25]. Ultrasonic treatment accelerates the removal of unconsolidated sand from asphaltenes and helps reduce the viscosity of the filtering fluid [26]. When filtering gas through artificial quartz rock samples in the field of elastic vibrations at a frequency of 40 kHz, the permeability of the samples was restored by 30% [27]. Mechanical mixing provided by 20 kHz acoustic waves re-disperses the wax and restores the effective permeability of Berea sandstone samples to their intact state [28]. Due to the decrease in oil viscosity, interfacial tension, and microstructural changes in the rock, elastic vibrations have a positive enhancing effect on oil recovery during waterflooding [29] samples. Ref. [30] proposes to use nonlinear loading in conjunction with the injection of gas, liquid, or other chemicals. On the large-scale experimental rig [31], it was found that the nonlinear loads increase the fluidity of oil by a factor of 2-3 and increase the oil filtration rate through the porous medium by a factor of 1.2-1.5, arguing that narrow channels in the rocks of the borehole are unblocked (removed) from waves and asphaltenes, clay particles, and other compounds. Among state-of-the-art studies, 2D micromodels gain ground due to the visibility of the process. Using such a model, ultrasonic waves with a frequency of 20 kHz are able to remove the pore space from deposited asphaltenes, and the amount of removed asphaltenesincreases with increasing treatment time (sonication) [32]. The authors also showed that the geometry of the pore space significantly affects the efficiency of ultrasonic treatment. Using a micromodel with an ultrasonic device, the oscillations reverse asphaltene deposition on the pore wall and remove asphaltenes from the pore channel, increasing oil production [33]. Micromodels allow one to visually demonstrate the influence of a nonlinear load in a flat, artificially porous medium. At the same time, the pore space of the rock is much more complicated, and therefore it is of particular interest to evaluate the effect of nonlinear loading on a conventional core sample. To study the influence of nonlinear loads on the filtration of a liquid in a porous medium, it is always required to build an experimental set-up containing a source of elastic waves. To study the fluid flow through rock samples under nonlinear loads [34; 35], we made an attempt to design an experimental set-up. In this work, a laboratory rig is built, and the dependence of rock permeability and the intensity of paraffin and asphaltene precipitation on ultrasonic treatment are investigated. Section 2 describes the materials and methods used in this study. Section 3 presents the results obtained and their discussion, followed by a conclusion.
## 4 Results
### Comparison of Annual Deformation Rate
Most of the differences are within \(\pm 30\) mm, and the data consistency is relatively poor. In order to analyze the difference in deformation rate between the Sentinel\(-1\)A and ALOS/PALSAR\(-2\) data in more detail, three areas where the deformation is concentrated (Zone 1, Zone 2, and Zone 3 in Figure 6) are investigated more closely. In Zone 1, which has low vegetation coverage, although Sentinel\(-1\)A is able to recognize most of the deformed areas detected by ALOS/PALSAR\(-2\), it found deformation-rate values and landslide-activity ranges that were generally smaller than those detected by the latter. In  
Figure 6: Diagram of annual LOS deformation rate. (**a,b**) show results from Sentinel\(-1\)A and ALOS/PALSAR\(-2\), respectively. The right side contains the enlarged diagrams of the corresponding zones (Zone 1, Zone and Zone 3). this case, the rate of missed judgments in subsequent interpretations would increase. In Zone 2, which has medium vegetation coverage, Sentinel\(-\)1A missed six large deformation areas that ALOS/PALSAR\(-\)2 did not. In Zone 3, which has extremely high vegetation coverage, not only are the deformation rates of the two data sources quite different, but the effective monitoring area of Sentinel\(-\)1A is also greatly reduced. Sentinel\(-\)1A and ALOS/PALSAR\(-\)2 missed eleven and five concentrated deformations, respectively. In general, ALOS/PALSAR\(-\)2 has vast advantages over Sentinel\(-\)1A in detection of the deformation rate.
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 4 Security and Cryptanalysis on PQC KEM: How Kyber Was Selected
### PQC Requirements  
NIST evaluates post-quantum algorithms based on security, performance, and practicality. Among these, security is the most important. NIST aims to select algorithms that can withstand both classical and quantum attacks in real-world applications like TLS, SSH, and IPsec. Encryption and key-establishment schemes should be secure against indistinguishability under chosen ciphertext attack (IND-CCA2), and digital signature must be hard to forge. The second most important criteria are cost and performance, which refers to the efficiency with which the algorithm runs, memory use, key/ciphertext size, and decryption failure rates. Finally, practicality focuses on how usable and adaptable an algorithm is in real-world settings, including factors such as simplicity of implementation, cross-platform compatibility, and support for parallelism.
# Advancements and Applications of Rim-Driven Fans in Aerial Vehicles: A Comprehensive Review
## 8 Bearing  
Operational and design limitations pose significant challenges. One such challenge is bearing-related noise, which significantly impacts engine operation. To address this issue, researchers have explored various approaches, including modifying RDT hubs and utilizing bearings with a proportional radius.  
[PERSON] et al. developed a hubless-type RDT with ball bearings embedded in the duct [83]. Due to attrition in the bearings, the rotor was unable to achieve the specified speed at the rated voltage supply. [PERSON] inserted bearings into the hub (on both extremities of the hub) to address this issue (Figure 3), resulting in a reduction in both the friction resistance moment and friction loss [56].  
In a study, a bearing with a small proportional radius is used to modify the fan's centerline axis configuration [23]. The bearing is mounted on the stator body and rotates at the same speed. At the point of contact, the tangential speed of the fan and the bearing  
Figure 3: Inserted bearings into the hub (on both extremities of the hub) [85] (Copyright (2023), with permission from Elsevier).  
must be equal. This indicates that the rotational speed of the bearing is proportional to the ratio of the fan's radius to the bearing's radius. Therefore, a fan with a lower rotational speed requires bearings to rotate more, and this significantly higher rotational speed is responsible for a high noise band between 2 and 8 kHz. A slip bearing support was also used in this study. Current trends in acoustic research indicate that engine operation must be made silent by addressing bearing-related noise.  
For micro autonomous underwater vehicles designed in MIT [86], two options for bearings were considered: a long sleeve bearing and a pair of short sleeve bearings. The long sleeve bearing is built into the stator housing and placed between the stator poles and the rotor. It is smooth and in slide contact with the outer ring of the rotor. The rotor is slightly smaller to reduce mechanical friction. The long sleeve bearing can be made of Teflon or coated with it. The second option is a pair of short sleeve bearings placed at the front and back of the rotor like conventional motors. They have a radial component to prevent front and back movement and an axial component to maintain centeredness. They do not take up space in the air gap between the stator and rotor, allowing for better efficiency. A Teflon coating can also be applied to these bearings.
## 3. Tests and Results
### Tests

Table 2: Training results with hybrid dataset. Unlike Test 4 and 5, there are no obvious gaps between accuracies on the validation set and the test set in Test 9 and 10. The over-fitting problem of YOLOv4 with small training data is largely solved with the extra simulated samples. The accuracy on the test set can be improved to 85.00% with 238 experimental shearography images and to 75.46% with less than 100 (only 95) experimental shearography images. Therefore, when the experimental samples are less than 250 and are limited to enlargement in real applications, the simulation dataset can obviously improve the generalization of deep learning. The proposed simulation dataset preparation and hybrid training strategy will make deep learning more applicable for defect detection in digital shearography.
## 3 Data and Methodology
### The Proposed Methodology  
Basically, the phases of the proposed research scheme are: (a) data preparation (b) proposing an intuitive cost matrix, (c) adding cost sensitivity in different schemes to the classifier, and (d) assessing the resulted direct marketing prediction models. Figure 2 summarizes the main steps of the experiments.  
Several classification models were constructed and evaluated in order to capture the added value by adding cost sensitivity analysis to the conventional classification algorithm. In particular, two distinct cost-sensitive methods used, namely CostSensitiveClassifier and Meta-Cost. The characteristics of these two methods are illustrated in Table 3. Furthermore, both methods are elaborated in Sections 3.2.1 and 3.2.2 respectively. One base classifier is leveraged to evaluate each of those methods, which is MLP (Presented in Section 2.2). 10-fold cross validation scheme was maintained  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Variable** & **Description** & **Mean** & **SD** & **Percentile 25** & **Percentile 50** & **Percentile 75** \\ \hline age & Integer indicating client’s age & 41.17 & 10.576 & 33.00 & 39.00 & 49.00 \\ balance & Average yearly balance & 1422.66 & 3009.638 & 69.00 & 444.00 & 1480.00 \\ day & Last contact day of the month & 15.92 & 8.248 & 9.00 & 16.00 & 21.00 \\ duration & Duration of last contact in seconds & 263.96 & 259.857 & 104.00 & 185.00 & 329.00 \\ campaign & Number of contacts performed during this campaign and for this client & 2.79 & 3.110 & 1.00 & 2.00 & 3.00 \\ days & Number of days passed since last campaign contact & 39.77 & 100.121 & \(-1.00\) & \(-1.00\) & \(-1.00\) \\ previous & Number of contacts performed for this client from previous campaigns & 0.54 & 1.694 & 0.00 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numeric Variables (Portuguese Bank dataset [3]).  
Figure 2: The Proposed Methodology.  
in all experiments. The proposed approaches do not maintain instance resample either for training or for testing.  
Cost-sensitive algorithms deal with two types of cost factor, namely _FN-Cost Factor_ and _FP-Cost Factor_. While the former represents an estimated loss value if a potentially deposit-take willing client is incorrectly classified as not willing to subscribe a term deposit, the latter represents an estimated loss value if an unwilling client is incorrectly classified as willing to subscribe a term deposit. Consisting with the stated objectives and in accordance with the imbalance nature of the dataset, _FN-Cost Factor_ tuning is necessary. Usually, values of cost factors are set by domain experts. In this research, _FP-Cost Factor_ was fixed at _one_, meanwhile, nine distinct values of _FN-Cost Factor_ were examined, such that each classification model was evaluated against each _FN-Cost Factor_ value in order to achieve a trade-off between type I and type II errors. Ordinary base classifier, MLP, was used in all experiments without any particular parameter tuning. Experimental setup configurations are shown in Table 4.
# A Method for Detecting the Yarm Roll's Margin Based on VGG-UNet
## 3 Materials and Methods  
### Network Structure  
In 2015, fully convolutional networks (FCNs) [17] emerged as a pivotal innovation in semantic segmentation, marking a substantial advancement in the field. Building upon this foundation, [PERSON], [PERSON] et al. developed the U-Net model [18], tailored explicitly to meet the challenges of biomedical image segmentation. U-Net is highly regarded for its outstanding segmentation efficacy and swift training capabilities [19; 20; 21], making it ideal for use with small datasets. Consequently, this study adopts the U-Net model as the primary framework for detecting yarn roll margins. The U-Net model is distinguished by its symmetric architecture, comprising an encoder and a decoder. The encoder extracts features from the input image, successively generating feature maps at four distinct levels. Conversely, the decoder identifies these features through successive deconvolution and up-sampling processes. It integrates these features with corresponding feature maps from the encoder, thereby mitigating information loss during up-sampling and enhancing the model's segmentation precision. Furthermore, the model's feature extraction capacity is enhanced by integrating the first 13 convolutional layers of VGG16 [22] into the U-Net encoder, as shown in Figure 2, leveraging its pre-trained weights on large datasets for effective transfer learning [23]. This approach substantially boosts the model's performance and adaptability, particularly in specialized applications like yarn residual volume detection. In this study's implementation, as shown in Figure 3, the input yarn images are down-sampled by the encoder to generate four sets of feature maps at different scales: \(512\times 512\times 64\), \(256\times 256\times 128\), \(128\times 128\times 256\), and \(64\times 64\times 512\). In the decoder stage, these up-sampled feature maps are concatenated with the corresponding feature maps from the encoder layers to effectively fuse deep and shallow features. Finally, the number of channels in the feature maps is adjusted to match the number of target segmentation classes using a \(1\times 1\) convolutional layer to output precise segmentation results. The details of each layer are shown in Table 1. Through this approach, our model not only effectively improves the accuracy of yarn roll margin detection but also demonstrates strong capabilities in handling complex image segmentation tasks. Figure 2: The VGG16 architecture.
## 2 Bibliometric Analysis Research Methodology
### Bibliographic Data Extraction Process  
The fundamental aspect of bibliometric analysis constitutes the accumulation of bibliographic datasets from various scientific databases. The bibliometric dataset for this study, spanning the past 23 years, was compiled on 3 February 2025 using the ISI Web of Science, a highly esteemed scientific database in the academic community. Table 1 presents a detailed 11-step keyword search process, employing Boolean search terms.  
Step 1 started with the keyword \"Green Shipping Corridor\", which retrieved 21 articles. Step 2 expanded the scope slightly by adding \"Zero-Emission Shipping\", increasing the results to 41 articles. In Step 3, including \"Sustainable Maritime Transport\" accumulated the number to 76 articles, indicating that sustainability-related terms are more prevalent in the literature. Step 4 added more Boolean operators (ORs) and multiple sustainability-related terms, leading to 228 articles, showing a notable increase. By Step 5, adding \"Maritime Decarbonization\" yielded 269 articles, suggesting growing academic interest in decarbonization topics. Step 6 introduced \"Green Shipping\", which significantly increased the count to 528 articles, highlighting that \"Green Shipping\" is a widely covered topic. Step 7 added \"Low-Carbon Shipping\", raising the total to 598 articles, the highest at this point, reflecting the extensive literature on low-carbon initiatives in maritime contexts. In Step 8, applying the AND operator with \"Maritime\" reduced the results to 455 articles, indicating a more targeted search within maritime-related studies. Step 9 excluded non-English language articles, reducing the dataset to 454 articles, showing minimal non-English content, which implies the prevalence of English in the maritime sustainability literature. Step 10 limited the results to articles only (excluding conference papers, reviews, etc.), narrowing it further to 388 articles, focusing on peer-reviewed sources. Step 11 involved manual screening for relevance results in 238 articles, ensuring that only highly relevant studies were included in the final analysis.  
A careful and detailed examination of the latest edition of the \"Getting to Zero Coalition Annual Progress Report on Green Shipping Corridors 2024\" resulted in the selection of key phrases such as \"Green Shipping Corridors\", \"Zero-emission shipping\", \"Sustainable Maritime Transport\", \"Sustainable Shipping\", \"Maritime decarbonisation\", and \"Low-carbon Shipping\", which were then used as criteria for the manual filtering of articles [11]. Studies that were excluded related to the following: (1) Algorithm and Model Comparison; (2) Simulation and Computational Modeling; (3) Experimental and Empirical Investigations; and (4) Noise Review Analyses [27; 28; 29; 30].
## 4 Results
### Structural Equation Modeling (SEM)  
The one-factor test, suggested by [PERSON], was used in order to test if a common method bias (CMB) exists in the current study before SEM [86]. The test revealed that total variance was accounted for by a single factor less than 0.5, so there was no CMB issue. The current study conducted an SEM using the MLE, and the result is shown in Table 5. The results indicated that the model fits the data well (\(\chi^{2}(237)=609.820\), \(\chi^{2}/df=2.573\), \(p<0.001\), IFI = 0.948, CFI = 0.947, TLI = 0.939, and RMSEA = 0.071) [81,83]. The results also indicated that six paths were statistically significant, and two paths were not. PR (\(\beta=-0.247\) and \(t=-3.876\)) and QR (\(\beta=-0.229\) and \(t=-3.418\)) more specifically negatively affect AT, so H1a and H1b were supported. However, FR and TR had no significant effects on AT (\(p>0.05\)), so H1c and H1d were rejected. AT (\(\beta=0.457\) and \(t=8.644\)), SN (\(\beta=0.176\) and \(t=3.661\)), and PBC (\(\beta=0.574\) and \(t=9.617\)) positively influence BI in the TPB, and SN (\(\beta=0.430\) and \(t=7.859\)) also positively affect AT. Thus, H2, H3, H4, and H5 were also supported.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Construct and Scale Items** & **Standardized Loading** \({}^{\mathbf{a}}\) \\ \hline
**Perceived risk theory** & \\
**Psychological risk** & \\ PR1 & 0.950 \\ PR2 & 0.962 \\ PR3 & 0.919 \\
**Quality risk** & \\ QR1 & 0.930 \\ QR2 & 0.973 \\ QR3 & 0.942 \\
**Financial risk** & \\ FR1 & 0.891 \\ FR2 & 0.849 \\ FR3 & 0.932 \\
**Time risk** & \\ TR1 & 0.935 \\ TR2 & 0.861 \\
**TR3** & 0.849 \\
**Theory of planned behavior** & \\
**Attitude towards to ISFR** & \\ AT1 & \\ AT2 & 0.900 \\ AT3 & 0.882 \\
**Subjective norm** & \\ SN1 & \\ SN2 & 0.939 \\ SN3 & 0.953 \\
**Perceived behavior control** & \\ PBC1 & 0.743 \\ PBC2 & 0.798 \\ PBC3 & 0.837 \\
**Behavior intentions** & \\ BI1 & \\ BI2 & 0.907 \\ BI3 & 0.874 \\ \hline \hline \end{tabular} Note. \({}^{\mathbf{a}}\) All factors loadings are significant at \(p<0.001\) and measurement scales are presented in Appendix A.  
\end{table}
Table 3: CFA: items and loadings.
###### Abstract  
One of the challenges in predicting the remaining useful life (RUL) of rolling element bearings (REBs) is determining a proper failure threshold (FT). In the literature, the FT is usually assumed to be a constant value of an extracted feature from the vibration signals. In this study, a degradation indicator was extracted to describe damage to REBs by applying principal component analysis (PCA) to their run-to-failure data. The relationship between this degradation indicator and the vibration peak was represented through a joint probability distribution using statistical copula models. The FT was proposed as a probability distribution based on the fluctuation increase in the vibration trend. A set of run-to-failure tests was conducted. Applying the proposed method to this dataset led to various FTs for the different failure modes that occurred. It is shown that, for inner race degradation, a higher FT can be assumed than for rolling element degradation. This could help extend the lives of REBs regarding the degrading elements. A dataset for an industrial machine was also analyzed and it is shown that the proposed model estimated a reasonable and proper FT in an actual case study.  
rolling element bearing; remaining useful life; failure threshold; vibration fluctuation; principal component analysis; copula models; failure mode +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Failure Threshold Determination of Rolling Element Bearings Using Vibration Fluctuation and Failure Modes. _Appl. Sci._**2021**, _11_, 160. [[https://dx.doi.org/10.3390/app11010160](https://dx.doi.org/10.3390/app11010160)]([https://dx.doi.org/10.3390/app11010160](https://dx.doi.org/10.3390/app11010160))