esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
## 2 Integrated Bearing Dynamic Performance Analysis
### Impact of Operating Conditions on Vibration of Integrated Bearings  
The operating conditions of integrated bearings directly influence their vibration characteristics during operation. To investigate the specific effects of these conditions on the vibration characteristics of integrated bearings, this section examines the impacts of varying rotational speeds, radial forces, and axial preload on the vibration performance. The study considers an integrated ball bearing with a rotating inner shaft and a fixed outer ring, with both the inner and outer raceway curvature radius coefficients set at 0.529 and a  
Figure 11: Trend of vibration with variation in the curvature radius coefficient of the outer rolling path. (**a**) Trends in vibration velocity variation. (**b**) Trends in vibration acceleration variation.  
Figure 12: Trend of vibration with variation in the cage pocket clearance. (**a**) Trends in vibration velocity variation. (**b**) Trends in vibration acceleration variation.  
cage pocket clearance of 0.051 mm. The analysis focuses on the radial vibration acceleration level and vibration velocity at the centroid of the bearing shaft to characterize the impact of changes in operating conditions on bearing vibration.
## 4 Discussion
Extending the model to other disasters (e.g., floods, earthquakes) or regions would test its generalizability, while real-time data integration (e.g., mobile phone locations, facility occupancy) could improve dynamic disaster response. Integrating demand predictions with operational research models would optimize resource allocation and support decision-making in disrupted scenarios. Future research should also address data imbalances between lodging facilities and shelters using techniques like data augmentation or resampling to improve generalization and predictive performance. Policy and Practical Recommendations:The results of this study offer practical recommendations for policymakers and emergency management professionals, such as the implementation of real-time GPS data collection and analysis, which should be prioritized in disaster-prone regions to enable timely and accurate predictions of shelter demand. Additionally, the findings suggest a need for flexible shelter planning that accounts for potentially exceeding available capacity, particularly in critical shelters. Policymakers should consider establishing contingency plans that include the rapid deployment of additional non-congregate shelters or alternative lodging options. Some actionable recommendations for policymakers include infrastructure investments in advanced data integration platforms that can aggregate and analyze real-time information from various sources and prioritize the construction and maintenance of critical infrastructure, such as flood drainage systems, to mitigate disaster risks, reducing vulnerability and enhance community resilience [46]. They should promote interagency collaboration for effective communication and resource allocation during emergencies [47]. Leveraging technology such as big data analytics and artificial intelligence (AI) can help process information quickly, enabling rapid responses to natural disasters or health crises [48; 49]. Ongoing evaluation and updating of the predictive models should become a standard part of disaster management practices to ensure they remain effective in the face of evolving threats and changing environmental conditions.
## 3 Results
### 2D-SWE in the Evaluation of the Malignancy Risk of Thyroid Nodules  
The Mean EI values provided by the US system seem to have the best diagnostic performance (AUC: 0.912, CI: 0.836 to 0.961). We retrospectively obtained a cut-off value of >30.5 kPa that predicts malignancy with a sensitivity of 79.3%, specificity of 95.38%, NPV of 91.2% and PPV of 88.5%. The Max EI is also a good test, with an AUC of 0.877;  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Thyroid Cancer** & **Nr. of Cases** \\ \hline PTC & 22 \\ Multifocal PTC & 1 \\ microPTC & 3 \\ FTC & 1 \\ Hurthle cell carcinoma & 1 \\ NIFTP & 1 \\ \hline PTC—papillary thyroid carcinoma; microPTC—micropapillary thyroid carcinoma; FTC—follicular thyroid cancer, NIFTP— noninvasive follicular thyroid neoplasia with papillary-like nuclear features; nr—number. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of malignancies in the analyzed group.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & \\ \hline Maximum dimension (cm) & 1.9 (0.8-6.4) \\ Volume (mL) & 1.8 (0.15-44) \\ \multirow{3}{*}{Nodule size} & \textless{}1 cm & \(n=8\) \\  & 1–2 cm & \(n=44\) \\  & \textgreater{}2 cm & \(n=42\) \\ \multirow{3}{*}{Composition} & Solid & \(n=77\) \\  & Mixed & \(n=10\) \\  & Spongiform & \(n=7\) \\  & Hyperechocio & \(n=13\) \\ \multirow{3}{*}{Echogenicity} & Isoechoic & \(n=44\) \\  & Hypoechoic & \(n=32\) \\ \multirow{3}{*}{Homogeneity} & Markedly hypoechoic & \(n=5\) \\  & Homogeneous & \(n=46\) \\ \multirow{3}{*}{Margins} & Nonhomogeneous & \(n=48\) \\  & Smooth & \(n=61\) \\ \multirow{3}{*}{Calcifications} & Ill-defined & \(n=12\) \\  & Irregular & \(n=18\) \\ \multirow{3}{*}{Color-Doppler vascular pattern} & Infiltrative & \(n=3\) \\  & Macro- & \(n=6\) \\ \multirow{3}{*}{Shape} & Hyperechoic foci & \(n=15\) \\  & Absent & \(n=73\) \\ \multirow{3}{*}{Nodule growth} & Absent & \(n=11\) \\  & Mostly perinodular & \(n=41\) \\ \multirow{3}{*}{Color-Doppler vascular pattern} & Mostly intranodular & \(n=19\) \\  & Mixed & \(n=23\) \\ \multirow{3}{*}{Shape} & Taller-than-wide & \(n=8\) \\  & Wider-than-tall & \(n=86\) \\ \multirow{3}{*}{Nodule growth} & Moderate & \(n=27\) \\  & Yes & \(n=23\) \\ \multirow{3}{*}{Nodule growth} & Stable & \(n=44\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ultrasound characteristics of the analyzed thyroid nodules.  
a value above 40.3 kPa has a sensitivity of 86.2%, but a specificity of 81.5% for detecting thyroid cancer.  
The QBox ratio also represents a good parameter, with the AUC of 0.851 and a very good specificity of 92.3% for a cut-off value of 2.8. The data are detailed in Table 4.  
Figure 3 shows a comparison of the ROC curves for the three SWE above-mentioned parameters.  
An upgrade of the risk category was made in eight cases with increased stiffness, defined as values of Mean EI > 30.5 kPa. Six cases were upgraded from low to intermediate risk and ten cases from intermediate to high risk (see Figure 4).
# Filter Pruning via Attention Consistency on Feature Maps
## 2 Related Work
### Feature Map-Based Filter Pruning  
Feature map-based filter pruning methods utilize the prior information of training data to implement filter pruning strategies. For instance, [PERSON] [26] analyzes the filter contribution by the statistical information of the filter's output features and accordingly prunes filters with a smaller contribution. Sampling [27] proposes a sampling-based pruning method. It assigns a score to the significance of filters and constructs an importance sampling distribution where the filters with a higher probability of sampling are regarded as the more important ones. Another method, Channel Pruning (CP) [28] first samples the points on the feature maps, then retains significant filters by minimizing the reconstruction errors of the sampling points. The PFA [29] also prunes the secondary filter by setting the energy ratio of principal component analysis (PCA) which applies to feature maps. Subspace clustering is used to research the correlation among the feature maps, and then the filters generating redundant feature maps are pruned accordingly [30]. [PERSON] [31] also presented a simple pruning method to prune filters with lower-rank feature maps.  
Although feature map-based filter pruning methods are robust to different datasets, their performance is susceptible to noise. To solve this problem, a pruning method that focuses on the attention of the images is proposed.
## 3 Results and Discussion
### The Energy Saving Rate Based on Thermal Comfort  
To obtain the indoor thermal comfort requirements of a building, two methods are often used: (1) passive methods, which is to narrow the gap between outdoor climate and indoor thermal comfort through the building itself, such as increasing envelope insulation measures; and (2) active methods, which is to regulate through environmental equipment, such as raising the heating temperature. After several simulation cases, the paper obtained that after increasing the indoor air temperature of the original building model by 1.5 \({}^{\circ}\)C, the APMV value of original building could be close to that of the standard building. Therefore, this paper calculates the heat load, respectively, with these two different methods when achieving the same APMV, taking the original building with an indoor air temperature of 16 \({}^{\circ}\)C as a reference, and the results are shown in Figure 7.  
The results show that the energy saving rate of the standard building with the addition of the envelope insulation under the same indoor air temperature was 67%, and the cumulative heat load was increased 13% by raising the indoor air temperature of the original building to achieve the same indoor thermal comfort level. Raising the indoor temperature, on the one hand will increase the indoor APMV, and on the other hand will make the accumulated heat load during the heating season increase, which is not promoted from the perspective of energy saving. The temperature difference between indoor and outdoor increases makes the energy needs for space heating of the building increase, and the energy saving rate of the regulated building can reach 71% under the same thermal comfort.  
Figure 6: The dynamically inner surface temperature: (**a**) living room and (**b**) south facing bedroom.
# Slicing on the Road: Enabling the Automotive Vertical through 5G Network Softwarization
###### Abstract  
The demanding requirements of Vehicle-to-Everything (V2X) applications, such as ultra-low latency, high-bandwidth, highly-reliable communication, intensive computation and near-real time data processing, raise outstanding challenges and opportunities for fifth generation (5G) systems. By allowing an operator to flexibly provide dedicated logical networks with (virtualized) functionalities over a common physical infrastructure, network slicing candidates itself as a prominent solution to support V2X over upcoming programmable and softwarized 5G systems in a business-agile manner. In this paper, a network slicing framework is proposed along with relevant building blocks and mechanisms to support V2X applications by flexibly orchestrating multi-access and edge-dominated 5G network infrastructures, especially with reference to roaming scenarios. Proof of concept experiments using the Mininet emulator showcase the viability and potential benefits of the proposed framework for cooperative driving use cases.  
network slicing; V2X; 5G; MEC; SDN; Mininet +
Footnote †: journal: Accepted: 11 December 2018
# A Narrow-Down Approach Based on Machine Learning for Indoor Localization
## 5 Discussion and Conclusions  
A narrow-down approach is presented for indoor localization that involves coarse and accurate positioning phases. Training points' selection, area division, and overlapping strategies are presented to reduce the uncertainty in finding the actual user's location. In the coarse positioning phase, the SDR scheme involves data averaging, outlier removal, and stable APs' weight enhancement techniques, which are presented. The SDR scheme compressed 80% of classification training data and increased classification accuracy. In the final/accurate positioning phase, a set of MVR-DNN models, trained on each sub-area, is utilized to find the final user's location. Our experimental results establish the superiority of the proposed model over the existing machine learning approaches. It is worthwhile to find the proper size of clusters as the classification accuracy may vary with it. The selection of the optimal size and parameters of the neural networks, the effect of environmental dynamics, data collection overhead, and possible changes in signal strengths over time, which are dependent on a person's movements, as well as object displacements, an analysis of the bias effect to increase the weight of the RSS vectors of RPs, and a better area classification strategy could all be potential future research topics. Our proposed model is simple, generic, and flexible that can be applied to find the user's location coordinates accurately in any indoor environment.  
Conceptualization, methodology, validation and writing--original draft preparation by S.M.A.U. The investigation, resources, writing--review and editing by T.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData were obtained from a third party. The data are open-sourced and publicly available.  
The authors acknowledge the support provided by the University of Edinburgh for the experiments.  
The authors declare no conflict of interest.
# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 2 Materials and Methods
### Methodology  
The flowchart of this study is shown in Figure 5. This flowchart contains four steps: SAR image pre-processing (SARProcMod), water extraction using U-Net (WaterUNet), an accuracy assessment, and an analysis of the relationship between the water level and water area. The first step generates long-time-series Sentinel-1 SAR \(\
u^{0}\) images by using the SARProcMod model. Then, in the second step, a modified U-Net based water mapping model (WaterUNet) is proposed. To evaluate the performance of WaterUNet, some artificial samples are chosen for the confusion matrix and F1 score. Finally, we analyze the interannual and seasonal variation for the water body, as well as the relationship between the water area and the water level from Poyang gauging station.  
Figure 4: The temporal distribution and number of Sentinel-1 SAR observations per month between 2014 and 2021.
## 1 Introduction
It does not take into account the number and distribution of samples and is easy to implement. However, most studies focus on the construction of the grey correlation coefficients. If the shapes of evaluation sequences of some objects are very similar, the GCE method may not be able to evaluate them properly. For example, assume that the evaluation  
Figure 1: Technique for order preference by similarity to an ideal solution (TOPSIS) failed conditions. sequences U1, U2, and U3 are shown in Figure 2. The correlation degree between U2 and U1 is the same as that between U3 and U1. Other similar traditional methods, such as TOPSIS and GCE, assume that criteria are independent of each other. In this regard, to a great extent, it leads to limiting them when addressing some practical problems with interactive criteria. By considering these features of the TOPSIS and GCE approaches, this study develops a novel hybrid MCDM method called the fuzzy grey TOPSIS (FGT). By comparing the exiting methods, this research makes the following contributions. First, the proposed method combines the GCE and TOPSIS, which improves the one-sidedness of the GCE and TOPSIS methods. Decision-makers can make a decision according to their preference through adjusting a parameter that reflects the weights of the shape and the distance factors. Second, fuzzy measures and fuzzy integral are employed to express and integrate the interaction between the criteria that cannot be properly expressed through the existing methods. Third, fuzzy numbers are used to help the experts make a more reasonable and accurate evaluation of some qualitative criteria. The remainder of this paper is organized as follows: Section 2 is a literature review. Section 3 introduces the preliminaries. Section 4 presents the novel hybrid MCDM method that combines the fuzzy integral, the grey correlation, and TOPSIS. Section 5 utilizes an example of supplier evaluation of a collaborative manufacturing enterprise to illustrate the procedure, the feasibility, and effectiveness of the proposed method. Finally, conclusions are reached in Section 6.
###### Abstract  
Assessing the importance of nodes in urban rail transit systems helps enhance their ability to respond to emergencies and improve reliability in view of the fact that most of the existing methods for evaluating the importance of rail transit nodes ignore the disturbance effect of signaling system failures and are unable to objectively identify critical stations in specific disturbance scenarios. Therefore, this paper proposed a method for evaluating the importance of urban rail transit nodes in signaling system failure scenarios. The method was based on the research background of the signaling system failure that occurs most frequently and analyzed the network failure mechanism after the occurrence of a disturbance. The node importance evaluation indices were selected from the network topology and network operation performance in two aspects. The variation coefficient-VIKOR method was employed to comprehensively assess the significance of urban rail transit stations during signaling system failures. The Nanjing Metro network was also used as an example to evaluate the importance of network stations. The results showed that under the attack method of signaling system failure, most ECC and interlocking stations experienced significantly higher network performance losses compared to the original attack method, and a few interchange stations showed smaller performance losses. The critical stations identified based on the proposed method are mainly distributed in the passenger flow backbone of the Nanjing Metro and were constructed in the early stage; of these, 85% are ECC stations or interlocking stations, which are easily neglected in daily management, in contrast to interchange stations with heavy passenger flow. The results of this study can provide an important reference for the stable operation and sustainable construction of urban rail transit.  
urban rail transit; complex networks; signaling system failure; node importance; variation coefficient-VIKOR method +
Footnote †: journal: Applied  
_Applied_  
_sciences_
# A Review of Transformer-Based Approaches for Image Captioning
## 4 Transformers in Captioning  
Following the success of transformers in language modeling, they have been applied to several areas of computer vision. In image captioning, transformers are the backbone of the current high-performing models. Figure 1 highlights the evolution of captioning systems from the traditional encoder-decoder models to the basic transformer-based captioning systems to the transformer-based captioning models that employ vision-language pretraining (VLP). The VLP transformer-based models are the basis for the current state-of-the-art models. The approach of vision-language pre-training is characterized by the use of large-scale automatically-annotated datasets to pre-train the transformers prior to application in downstream vision-language tasks. It is interesting to note that the image feature extractor has been based on convolutional neural networks (CNN) for a long time, but recently there has been a shift towards Vision Transformers (ViT) for this function as well. This makes possible the realization of all-transformer models. The pipelines for each category shown in Figure 1 are paradigmatic and as such, most of the models we look at in this paper follow a similar flow. However, different models have incorporated various other features as well as changes in the fundamental architecture, which have improved the performance.  
In the rest of this section, we review the basic transformer-based captioning models and the models employing vision-language pre-training. We do not focus on the prior traditional captioning models (encoder-decoder-based), since those have been sufficiently reviewed in other works, such as [41; 43].
# Mechanism and Equivalence of Single Event Effects Induced by 14 MeV Neutrons in High-Speed QDR SRAM
## 3 Results and Analysis
### Mcu  
Figure 5 shows the MCU ratios of 14 MeV neutron, high-altitude and spallation source test. Clear differences can be seen. Compared with the high-altitude test, the ratio of 14 MeV neutron-induced double cell upset (denoted by MCU2) is about two times lower. Under the 14 MeV neutron irradiation, the largest MCU involves only 3 bits. While in the high-altitude test, the largest MCU involves 9 bits. The overall proportion of MCU induced by 14 MeV neutrons is 6.2%, which is lower than that of the high-altitude test (23.1%). It can be seen that the MCU probability and maximum size are both underestimated under the 14 MeV neutron irradiation, which may affect the design and verification of the error correction code (ECC) hardening scheme.  
The MCU shapes produced by 14 MeV neutrons are further extracted, based on reported MCU addresses and bitmap of the DUT. In Table 2, it can be seen that the MCU shape tends to be vertical. All the MCU2 exhibit vertical orientation. Figure 6 shows the  
Figure 5: MCU ratios of 14 MeV neutron, high-altitude and spallation source test.  
layout of sensitive volumes (SV) of the DUT with a checkboard pattern. The drain of the OFF-state NMOS is regarded as the memory cells' SV. It can be seen in Figure 6 that the SVs are gathering together. The lateral spacing between SVs (about 300 nm) is wider than the vertical spacing (about 100 nm), which is consistent with the above MCU shape feature.
# An In-Network Cooperative Storage Schema Based on Neighbor Offloading in a Programmable Data Plane
## 3 Proposed System Architecture
### System Architecture
After chunk data is stored in the storage service node, the mapping of the ID of the data chunk and the NA of the storage service node is registered to LNMRS for further retrieval (step7). * If there are no available neighbor nodes and the current node has no capability to deal with the data chunk packets, it will forward the packets to the least loaded neighbor and subtract 1 from the TTL field, which indicates the longest storage node path it can forward along. It is currently considered that the chunk data can only be offloaded once; thereafter, there is a need to wait for the data to be stored locally or forwarded to a unified flood discharge area in the cloud. The storage service nodes are assumed to have been deployed ahead of time, which is outside the scope of this paper. Similar processes can be executed in another local resolution area. It is assumed that traffic offloading is not possible between different resolution domains. Every storage service node can execute the offload process mentioned above in its own local resolution area, so that the entire network iteratively achieves load balancing. * Then, the consumer queries the GNMRS or LNMRS for the data chunk ID, obtains the NA of R2, and obtains chunk data from R2.