esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Based on research and development and the application of high-altitude and high-reliability intelligent hydropower units--and taking a river basin Francis turbine as the research object--the impact of varying degrees of blade wear on the fluid excitation forces acting on the turbine runner system and the vibration response of the main shaft system was investigated. The main research conclusions are as follows:  
1. The change in the blade wear degree mainly affects the rotation frequency and blade frequency of the fluid excitation force. The frequency change mainly occurred during the transition from no wear to wear of 3.209 mm, which increased by 1.04 times and 0.71 times, respectively. Thereafter, as the wear extent further increases, both components exhibit a gradual increase trend in the amplitude.
2. In the wear range covered by this study, the amplitude of the radial exciting force under the rotation frequency increases by about 2.05 times, and the amplitude of the blade frequency increases by about 1.67 times. However, the frequency of the guide vanes has little correlation with blade wear, and its amplitude has no obvious change trend.
3. As the turbine runner blades gradually wear from unworn to a maximum wear thickness of 22.461 mm, the peak-to-peak amplitude in the x-direction of the turbine runner system under fluid excitation forces increases by 1.01 times, while that in the y-direction increases by 0.86 times. Spectral analysis reveals an increase of 95.14% in the amplitude at the rotation frequency of the turbine runner system. The maximum deviation distance of the shaft center trajectory increases by 31.67%. Based on the amplitude degradation trajectory model, the pseudo-failure time of the shafting failure of hydropower units is estimated to be about 55,139 h. The running state of the runner system is predicted according to the formula of blade wear. When the blade wear thickness reaches 24.56 mm, the runner system will reach the failure state.  
Conceptualization, W.C. and J.P.; methodology, J.P. and S.W.; software, S.W., J.M. and H.C.; validation, S.W., H.C. and W.Z.; formal analysis, S.W., J.M. and W.Z.; investigation, S.W., J.M. and H.C.; resources, W.C. and J.P.; data curation, S.W.; writing--original draft preparation, S.W.; writing--review and editing, S.W. and J.P.; visualization, S.W. and H.C.; supervision, W.C. and J.P.; project administration, J.P.; funding acquisition, W.C. and J.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Special Support Plan for High-Level Talents in Zhejiang Province (No. 2021R52036).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors would like to thank Zhejiang Sci-Tech University.
## 2 Numerical Simulation and Tests
### Description of Geometry Models
The frozen rotor setting was imposed on the interface dynamic and static components to simulate real relative position movement. Besides that, mass and momentum were set to no slip, and the wall roughness was set to the smooth wall setting. The advection scheme was set to high resolution and upwind. The solution convergence criterion was set to \(10^{-5}\). The Scale-adaptive simulation (SST - SAS) model was adopted for unsteady calculation, and the initial value of the unsteady calculation was the steady calculation result of the corresponding working conditions. The MRF model of multi-reference coordinate systems was adopted for the simulation of dynamic and static components. Rotor-stator interaction was imposed on the interface between the runner, the volute, and the draft tube. The impeller rotation time of \(2^{\circ}\) was taken as a time step, and the calculated time selected was 10 rotation cycles.
# Concrete Cracks Detection Based on FCN with Dilated Convolution
## 4 Experiments
### Experimental Preparation
#### 4.3.2 Experimental Design  
In order to select the backbone network and verify the effectiveness of the algorithm, we designed four models for experiments: (1) The model called FCN-Resnet-18, which removed dilated convolution and feature fusion, and only retained residual network as encoder. As shown in Figure 3, the feature maps F4 were the output of the encoder. (2) The model called FCN-Resnet-50, which is similar to FCN-Resnet-18. The only difference between FCN-Resnet-50 and FCN-Resnet-18 is that the residual network in FCN-Resnet-50 has more layers. In FCN-Resnet-50, we utilized Resnet-50 as the backbone network, and the specific structure can be seen from [10]. It should be noted that AvgPool was used for down-sampling, which ensured that the down-samplings of Resnet-50 were consistent with down-samplings of Resnet-18 we used. (3) The model called FCN-VGG, which is a classical semantic segmentation algorithm and uses VGG16 as an encoder. (4) The proposed model, which combines Resnet-18 and dilated convolutions that have different dilation rates as the encoder. That is to say, the proposed model is an improved version of FCN-Resnet-18. The encoder structure is shown in Figure 3. It should be further explained that, unlike the proposed model, FCN-Resnet-18 and FCN-Resnet-50 performed down-sampling before outputting the feature maps F4, that is, the feature maps output from the encoder of FCN-Resnet-18 and FCN-Resnet-50 were 1/32 of the input image. Similarly, the feature maps output by the encoder of FCN-VGG were 1/32 of the input image. In order to be able to access the up-sampling module, a deconvolution layer was used to enlarge the feature maps with a size of 1/32 of the input image, so that their size became 1/16 of the input image after the deconvolution layer. It should be noted that the deconvolution layer used includes deconvolution, BatchNorm, and ReLU. The encoder VGG is shown in Figure 10. The comparison of parameters, layers, and training time of different models is show in Table 1. It should be noted that the training time in seconds refers to the time required to complete training of 400 epochs. In addition, the number of network layers we counted included convolution layer, average pooling layer, and deconvolution layer.  
Figure 10: The framework of VGG.
## 4 Results and Discussion
### Vertical Swelling Pressure  
#### 4.3.1 Effect of Swelling Time  
Figure 13 shows the relationships between vertical swelling pressure and swelling time under different initial dry densities for MB specimens. At the beginning of the test, the structure of the MB specimen gradually changed with the infiltration of saline water, at the same time, the specimen was restricted by the rigid wall and this produced a vertical swelling pressure. In the initial stage, the swelling accelerated, and the vertical swelling pressure increased rapidly. Over time, the sample entered a slow swelling stage, when the swelling pressure reached a certain value, the rate of change of vertical swelling pressure decreased, the MB specimen tended to be saturated and the corresponding vertical swelling pressure stabilized. The experimental results showed that the vertical swelling pressure of MB specimens presented stage-changing characteristics over time, as found elsewhere by studies exploring the relationship between swelling pressure of NB and swelling time under fresh water [44; 45].  
By further analysis of the change in swelling pressure with swelling time for MB specimens under different initial dry densities, it was found that the ratio of time to vertical swelling pressure showed a linear relationship with swelling time (Figure 14):  
\[\frac{t}{P}=a+b\times t \tag{13}\]  
Figure 12: Comparison between calculated, and experimental, values.  
where \(P\) is the swelling pressure (kPa), and fitting parameters \(a\) and \(b\) are listed in Table 6.
## 1 Introduction
### Contributions  
This paper proposes PFL with an attention mechanism. As each vehicle gathers diverse data with varying statistical characteristics, challenges arise in effectively generalizing the global model at the UAV. The global model may yield suboptimal performance due to differences in data distributions and transmission patterns. This degradation in the global model becomes significant as the diversity among local data from different vehicles continues to increase [14]. The attention weights capture the relationship between the hidden states and the past trajectories of the UAV. By leveraging the collective data from all participating vehicles, the proposed approach integrates an attention mechanism to enhance collaboration among vehicles with similar data distributions and mitigates data scarcity and straggler issues [26]. Instead of using a single global model to aggregate local updates, the UAV stores several personalized models, which encapsulate common data from multiple vehicles [75]. The UAV aggregates several global models based on the data distribution parameters calculated by each vehicle [76]. The principal contributions are listed as follows:  
1. For PFL model convergence, optimal policy evaluation, and improvement, we consider the multi-objective MDP as a sequence of parameterized optimization problems. The optimal solutions correspond to the optimal actions in the proposed bin-packing problem (BPP). This leads to an improvement of state evaluation and action evaluation in a reduced action space.
2. We investigate the vehicle selection losses and communication errors in the PFL model convergence rate. We identify the factors that impact the global optimum and minimize training loss [77].
3. We analyze the importance of past UAV trajectories in the proposed system using the _V2X-Sim_ dataset from [78] and the _LTE I/Q_ dataset from [79]. Consequently, we develop a sequential prediction model for future UAV trajectories.
4. We extend our previous works in [80,81] by incorporating FL with an attention mechanism and predict the UAV trajectories that lead to maximum utilization of available battery power [82].
# Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis
## 3 Results
### Triple Collocation Analysis
#### 3.2.2 Results of TCA
Both the Merged and NOAA products have negative biases at higher winds compared to SAR and SMAP, although NOAA's was smaller at \(\sim\)1 m/s compared to \(\sim\)2 m/s for Merged. The RMSD comparing Merged and SMAP (Figure 5b) is 4.19 m/s, which is lower than the 6.92 m/s reported in [30], possibly because SMAP values from Remote Sensing Systems instead of from JPL were used in their study. Separating these plots by times when SMAP is above or below 17 m/s gives more specific insights to performance in TCs. Below 17 m/s, NOAA and Merged performed similarly, with Merged having a slightly lower RMSD of 3.1 compared to 3.4 for NOAA. Above 17 m/s, NOAA had higher Pearson correlations and much smaller biases. When SMAP was above 17 m/s, CYGNSS merged had a bias of \(-\)4.5 m/s to SAR and \(-\)4.3 m/s to SMAP, and this underestimation is made clearest in Figure 5a. In comparison, NOAA had biases of \(-\)1.7 m/s to SAR and \(-\)2.6 m/s to SMAP. The Merged product above 17 m/s has greater similarity to SMAP than SAR, whereas NOAA had equal performance to both datasets. The Merged algorithm seems to saturate and provides no values recorded above 40 m/s (Figure 5a,b), which is consistent with previous findings [55], while NOAA still records some values at these very high wind speeds. The SAR-SMAP matchups (Figure 5c,f) reveal a good relationship between these two datasets, with Pearson correlations of about 0.8. There is a large difference in the SAR-SMAP matchups (Figure 5c,f), which may be attributed to different spatial coverage between the two algorithms. This suggests that the data processing in Merged limits it to areas where there is higher agreement with SAR and SMAP. The R and RMSD of the SAR-NOAA product (Figure 5d) are even greater than in Table 2 because all speeds are included, whereas Table 2 only considered SAR winds \(>\) 15 m/s.
# Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation
## 2 Methods
### The Transfer Learning Framework for RUL Prediction
#### 2.3.3 Evaluation  
To evaluate the performance of our RUL estimation model on the test data, two measures are used here: Scoring Fuction and Root Mean Square Error (RMSE).  
The scoring function proposed in PHM 2008 Data Challenge [37] is shown in Equation (10), where \(n\) is the total number of trajectories, \(h_{i}=RUL_{est,i}-RUL_{i}\), \(RUL_{est,i}\) is estimated RUL and \(RUL_{i}\) is the true RUL. This scoring function gives two different penalties. The \(h_{i}<0\) penalty (estimated RUL is less than the true RUL) is smaller than the \(h_{i}\geq 0\) penalty (estimated RUL is larger than true RUL). The justification for this difference is that when \(h_{i}<0\), we still have time to conduct system maintenance; but when \(h_{i}\geq 0\), the maintenance would be scheduled later than the required time, which may cause system failure.  
\[S=\begin{cases}\sum_{i=1}^{n}(e_{i}^{-\frac{h_{i}}{h_{i}}}-1)&\text{for }h_{i}<0\\ \sum_{i=1}^{n}(e_{i}^{\frac{h_{i}}{h_{i}}}-1)&\text{for }h_{i}\geq 0\end{cases} \tag{10}\]  
RMSE is also widely used as an evaluation metric for RUL estimation, as shown in Equation (11). RMSE gives the same penalty weights for \(h_{i}<0\) and for \(h_{i}\geq 0\).  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}h_{i}^{2}} \tag{11}\]  
We used the mean score function as the loss function for training our networks, as shown below.  
\[loss=S_{mean}=S/n \tag{12}\]  
where \(S\) is calculated from Equation (10), n is the total number of trajectories.  
Figure 3: Extracting input samples.
## 3 Experiments and Results
### Coupling Modeling  
Following the algorithmic screening based on correlation analysis, a dataset was crafted utilizing nine selected factors. The DNN neural network model was then applied to generate the landslide susceptibility zoning map for Moxi Town, as illustrated in Figure 4a. This map was stratified into five susceptibility levels. The identified causative factors were subsequently reorganized into five distinct groups, drawing upon the collective wisdom from prior susceptibility assessments. A tally of the raster counts for each factor group was conducted, and the information content for each group was calculated using the specified formula, with the outcomes presented in Table 3. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Correlation Coefficient** & **Elevation** & **Slope Orientation** & **Slope** & \begin{tabular}{c} **Fault-** \\ **Influence** \\ **Distance** \\ \end{tabular} & **Relief** & **Plan** & **Profile** & \begin{tabular}{c} **Distance** \\ **Curvature** \\ \end{tabular} & \begin{tabular}{c} **Road-** \\ **Curvature** \\ \end{tabular} & \
\begin{tabular}{c} **Mered by** \\ **Water System** \\ \end{tabular} &
\begin{tabular}{c} **Impact** \\ **Distance** \\ \end{tabular} \\ \hline Elevation & 1 & 0.961 & 0.325 & 0.371 & 0.165 & 0.534 & 0.529 & \(-\)0.47 & 0.306 \\ Slope Orientation & 0.961 & 1 & 0.138 & \(-\)0.475 & \(-\)0.011 & 0.703 & 0.703 & 0.594 & \(-\)0.449 \\ Slope & 0.325 & 0.138 & 1 & \(-\)0.656 & 0.704 & 0.386 & \(-\)0.48 & 0.252 & 0.269 \\ Fault-Influence & 0.371 & 0.475 & 0.656 & 1 & \(-\)0.768 & 0.671 & 0.857 & \(-\)0.192 & \(-\)0.254 \\ Distance & 0.165 & 0.011 & 0.704 & \(-\)0.768 & 1 & 0.515 & 0.678 & \(-\)0.247 & 0.205 \\ Plan Curvature & 0.534 & 0.739 & 0.386 & 0.671 & \(-\)0.515 & 1 & 0.952 & \(-\)0.54 & 0.546 \\ Profile Curvature & 0.529 & 0.703 & \(-\)0.48 & 0.857 & \(-\)0.678 & 0.952 & 1 & \(-\)0.385 & 0.276 \\ Distance Affected & \multirow{2}{*}{0.47} & \multirow{2}{*}{0.594} & \multirow{2}{*}{0.252} & \multirow{2}{*}{\(-\)0.192} & \multirow{2}{*}{\(-\)0.247} & \multirow{2}{*}{\(-\)0.54} & \multirow{2}{*}{0.385} & \multirow{2}{*}{1} & \multirow{2}{*}{\(-\)0.477} \\ By Water System & & & & & & & & & \\ Road-Impact Distance & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Factor information scale.
## 7 Feature Extraction Approaches
### Local Direction Encoding-Based Approaches
Subsequently, the multi-feature two-phase sparse representation (MTPSR) was designed to improve the overall matching cost and to allow the handling of palmprint feature recognition. Table 5 furnishes an overview of the studies discussed in this subsection, outlining the employed feature extraction methodologies, datasets, experimental protocols, and key findings. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Paper**} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Used Datasets**} & \multirow{2}{*}{**Evaluation Protocol**} & \multirow{2}{*}{**Acc. (\%)**} \\ \cline{3-4} \cline{6-9}  & & & **Name** & & **\#IDs.** & & & \\ \hline
2004 & \
\begin{tabular}{c} [PERSON] and \\ [PERSON] [59] \\ \end{tabular} & PalmCode & Private & 40 & 800 &
\begin{tabular}{c} 1 Img/sub for training, remaining \\ for testing (both palms) \\ \end{tabular} & 98.00 \\ \hline
2006 & [PERSON] et al. [60] & Fusion Code & Private & 488 & 9599 &
\begin{tabular}{c} First session for training, second \\ session for testing \\ \end{tabular} & 98.26 \\ \hline
2011 & \begin{tabular}{c} [PERSON] et al. \\ [61] \\ \end{tabular} & CT + NSCT & \
\begin{tabular}{c} PolyU \\ \end{tabular} & 386 & 7752 &
\begin{tabular}{c} 5 Imgs/sub for training, \\ remaining 3 for testing \\ \end{tabular} & 88.91 \\ \cline{3-4} \cline{6-9}  & & & & & & & \\ \cline{3-4} \cline{6-9}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A comparative overview of local direction encoding-based approaches (#ID: number of identities, #Imgs: number of images, Acc: valid accuracy).
# Video Quality Modelling--Comparison of the Classical and Machine Learning Techniques
## 1 Introduction  
The demand for video content is growing rapidly, which is why most platforms embed and distribute videos on apps to increase views and engagement. According to a report published in January 2023 by Sandvine, an application and network analytics company, video represented almost 66% of the total online volume in the first half of 2022 [1]. Previous CISCO forecasts said that video traffic on the Internet would increase by more than 80% [2]. The Mobility Report from Ericsson (June 2024) shows that mobile network data traffic grew 25% between Q1 2023 and Q1 2024, and at the end of 2023, video traffic accounted for 73% of all mobile data traffic. This traffic growth is driven by both increasing smartphone subscriptions and increasing average data volume per subscription, fuelled primarily by increased viewing of video content [3]. While forecasts vary, they all indicate that Internet traffic is growing rapidly, with the majority (currently three-quarters to four-fifths) of traffic related to video content delivery. Such a high network load, as well as the different quality of video recordings resulting from the coding technique used, bitrate, and resolution, may result in insufficient video quality for the end user. Poor quality can cause negative user feelings and reduce interest in video transmission. Therefore, in order to provide theend user with appropriate video quality, it is important to be able to assess and control this quality.  
The main goal of the work is to find a method for modelling video image quality based on measurable objective parameters, which will enable users to predict the quality assessed to a better extent than the existing methods.  
The main contribution is building a machine learning model that, on the one hand, is able to take into account much larger amounts of data than classical quality modelling methods and thus achieve greater convergence with the subjective assessments of users and, on the other hand, can help automate the video quality assessment process.  
The remainder of the paper is organised as follows: Section 2 provides a reference to the literature on video quality assessment issues and the methods used. The next part of the article presents a general outline of the research methodology used by the authors, along with a description of research materials and methods. In Section 4, the authors present the results of the research. Section 5 contains a discussion of the results obtained in relation to previous research and other publications. The final part of the work presents the most important conclusions from the research and indicates directions for further work.
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 4 Methodology
### Improved Cosine Text Similarity Implementation
#### 4.5.2 Bucket Category Concept  
A new strategy was also designed, named bucket category selection, that extends and improves the multiple runs algorithm. In each run of the multiple runs algorithm, for each one of the top-5 ICD-10 codes retrieved, the parent ICD-10 category is also retrieved (one level above the ICD-10 code). Afterwards, the similarity is calculated between the EHR and all the ICD-10 codes that are under the ICD-10 code's parent category, forming what we call a bucket of codes. The codes in the bucket are then sorted by similarity and up to 40 of them are considered positive candidates. Hence, the bucket selection works as a way of lowering the similarity threshold to codes of a given category, based on the observation that neighbouring codes are often found in EHR. Figure 1 shows the flowchart of the algorithm, with the bucket category concept.  
For clarification of how the bucket category concept works, let us consider the following case, where the ICD-10 codes source of truth are, \"S72012A\", \"W010 XXA\", \"Y93K1\", \"Y92480\", \"K219\", \"E7800\", \"I341\", \"G43909\", \"Z87891\", \"Z87442\", \"F419\", \"M810\", \"Z7901\".  
When calculating the Cosine similarity between preprocessed text and ICD-10 code descriptions, \"K21\", \"I341\", \"M2535\" and \"M21259\", were identified with the highest similarity. The ICD-10 categories where these codes belong to, namely \"K20-K31\", \"I30-I52\"and \"M20-25\", are retrieved, and form buckets with all the ICD-10 codes that belong to each category. With the codes that form each bucket, the Cosine similarity between the bucket codes description and the preprocessed text is calculated. After this, for the category \"K20-K31\", it is found that \"K219\", is included in the top-40 bucket similarity. And for category \"I30-I52\", code \"I341\" is also retrieved. In this case there are two extra true positives.