esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Diffusion Multi-Distribution Filter with IMM for Heavy-Tailed Noise
## 1 Introduction
[19] proposed a filter based on multiple measurement models and multiple process models, which can simultaneously address both model uncertainty and measurement uncertainty. Similarly, [PERSON]. [20] introduced a robust filter by combining Gaussian and Student's t distributions. However, neither of these methods considers the influence of historical models on current outcomes as the IMM approach does. Moreover, the application of hybrid models in distributed scenarios remains an open problem. Combining IMM and hybrid models can better handle outliers caused by maneuver, thereby making tracking more accurate. Besides, there are numerous related studies aimed at improving the performance of filtering [21; 22; 23; 24; 25]. Presently, several pressing challenges remain in the realm of maneuvering target tracking. Building upon the discussion of existing filtering algorithms in the above, this paper seeks to address some of these issues through algorithmic enhancements: First, to address potential outliers arising from human-induced maneuvers, a Gaussian and Student-\(t\) mixed distribution filter is utilized to effectively process heavy-tailed noise. Second, to account for the uncertainty of target motion, a distributed sensor network coupled with a diffusion fusion algorithm is employed to dynamically capture targets while mitigating the impact of estimation errors at partial nodes. In light of the research presented, this paper proposes a distributed diffusion Multi-Distribution Filter with IMM (DDMDIMM) for addressing the problem of maneuvering target tracking with heavy-tailed noise. In short, the main contributions of this work are as follows:1. The integration of a multi-noise distribution filter with the IMM algorithm enhances fidelity to realistic tracking scenarios characterized by predominantly maneuvering targets and heavy-tailed observation noise. 2. The diffusion algorithm used is robust and insensitive to local deviations caused by outliers, resulting in greater accuracy. 3. The simulation verifies the functionality of the designed filter.
## 4 Numerical Examples and Validation of the Software
### Examples in 2d  
In this section, two examples will be discussed for the parameter sets that are given in the Tables 1 and 2. For the treatment of the augmented Lagrangian algorithms, the same parameters were used as before. Furthermore, in every numerical example in this section, the estimators \(\eta\) and \(\sigma\) from remark and Definition 1 were used. Furthermore, note that the refinement algorithms that were implemented in [55] were used to treat the examples of the adaptive scheme.  
For the numerical examples in 2d, similar breaking conditions on the algorithms were set:  
1. The breaking condition from **S3** in Algorithm 1 is fulfilled with a tolerance \(\varepsilon:=10^{-4}\).
2. The number of nodes in the current mesh increases 1000 nodes.
3. More than 100,000 iterations of the augmented Lagrangian algorithm were performed.  
In contrast to the examples in 1d, a lower number of nodes was used as breaking condition for the refinement algorithm.
# Fatigue Life Analysis of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrications
## 4 Result Calculation and Analysis
### Influence of Elastohydrodynamic Lubrication Conditions on Contact Load Distribution in Bearings  
Figure 9 presents the load distribution between cylindrical rollers and the bearing inner ring under the cylindrical roller bearing parameters specified in Table 2 and operating conditions listed in Table 3.  
As shown in Figure 9, calculations using the De Mul model reveal that only roller No. 5 to No. 11 exhibit contact loads with the bearing inner ring. This occurs because the bearing inner ring compresses the lower half of rollers under radial loading, leaving the upper half rollers not in contact with the inner ring and consequently free from elastic deformation. In contrast, the results obtained from the theoretical model proposed in this study demonstrate that, excluding roller No. 1 to No. 4 and No. 12 to No. 13, the remaining rollers maintain minor contact loads with the inner ring. This phenomenon arises because, under lubricated conditions, the formed oil film acts on the rollers even when the geometric approach between the upper rollers and the inner ring remains positive (non-direct contact).  
Figure 8: Film pressure of rollers and bearing rings at different positions.  
Figure 10 illustrates the load distribution between cylindrical rollers and the bearing outer ring under the cylindrical roller bearing parameters in Table 2 and operating conditions listed in Table 3.  
As shown in Figure 10, persistent contact loads exist between cylindrical rollers at all positions and the bearing outer ring. This phenomenon is attributed to centrifugal forces (216.5 N) that press the cylindrical rollers against the outer raceway, thereby maintaining continuous engagement with the bearing outer ring.  
Through the incorporation of elastohydrodynamic lubricated conditions between rollers and bearing rings, the theoretical model proposed in this study yields contact loads that marginally exceed those calculated by the De Mul model in overall magnitude.
# Spatial Unmasking Effect on Speech Reception Threshold in the Median Plane
## 2 Method
### Stimuli  
The target speech signals were selected from a set of 720 high-context sentences from the Harvard IEEE corpus [14], which was also used by [PERSON] et al. [4] in their speech intelligibility experiments. Any sentences with pronunciations non-standard to British English were removed. The broadband speech recordings, all at 44.1 kHz sample rate and ranging between 1.3 and 2.6 s, were scaled for equal root mean square (RMS) value. An example sentence is as follows: \"TAKE the WINDING PATH to REACH the LAKE\". The key words are displayed in capitals. There were five key words in each sentence.  
Noise masker signals were generated uniquely for each target, as in [PERSON] et al. [4], a speech-shaped noise was used as the masker. An FFT was performed for each target and the phase components were randomised, producing noise with the same spectral content and length of the target. [PERSON] et al. [4] used a global noise masker with an average frequency content generated from the entire target database. In the current study, however, a speech-shaped noise masker was produced specifically for each target. This ensured consistency in target sentence evaluation whereby no single target sentence would be easier or more difficult to decipher with respect to its noise masker. This was considered to be important since the focus of the study was on the relative differences of SRT depending on the elevation angle of the masker, rather than an absolute SRT for each target.
## 5 Conclusions  
In conclusion, a transition frequency identification of postural control from an ankle to hip strategy was presented. Two types of data were used: the first was COM-COP displacements and the second was ankle-hip angular displacements. These data were analyzed to obtain CCF coefficient data under increasing and decreasing continuous translation surface perturbation at different frequencies. The transition point of postural control strategies was identified based on the transition phase of CCF data (in-phase to anti-phase). This was achieved by implementing the logistic function of the sigmoid model to differentiate the upper and lower distribution of CCF coefficient data. Two tests were performed to determine the significant difference between frequencies and types of data; the first was a paired \(t\)-test and second was a two-way ANOVA. It was observed that ankle-hip angle opposed the COM-COP displacements during increasing and decreasing perturbation frequency, where the COM and COP displacements were decreased during increasing frequency perturbation while the ankle and hip angle displacement were increased and vice versa for decreasing frequency perturbation. Besides, the peak-to-peak amplitude of head displacement was decreased as the perturbation was increasing and vice versa. At some instant, there is a sudden change in the 'phase angle' of the data, where either the ankle strategy becomes passive or the hip becomes active. Based on the results, the mean transition frequency of COM-COP and ankle-hip for all subjects was determined. It showed the transition of COM-COP was evident at 0.44 Hz and the transition of ankle-hip was at 0.42 Hz during increasing perturbation. Meanwhile, a transition frequency of 0.55 Hz (COM-COP) and 0.56 Hz (ankle-hip) was observed during decreasing perturbation. Therefore, it was concluded that the transition frequency of postural control strategies for both the COM-COP and ankle-hip data of healthy subjects was apparent between 0.4 Hz and 0.6 Hz. The transition frequency was varied according to the direction of the platform frequency. Besides, the variation in the transition frequency of each subject occurs because there is no specified condition implemented that can normalize the posture control strategy such as a light touch and body-harness support. In addition, the implementation of a small step size of 0.02 Hz during increasing and decreasing continuous translation surface perturbation gives a more accurate reading of transition frequency as compared with previous studies, which indicated that the range of transition happened at 0.5 Hz and above. This study provides a new approach for analyzing the transition phase of postural control strategies.  
Conceptualization, M.A.M.D.; data curation, N.F.F.M.R.; formal analysis, N.F.F.M.R.; funding acquisition, S.-I.Y.; investigation, N.F.F.M.R.; methodology, N.F.F.M.R.; software, M.A.M.D.; supervision, S.-I.Y.; validation, N.F.F.M.R.; writing--original draft, N.F.F.M.R.; writing--review and editing, S.-I.Y.  
This research received no external funding.  
The authors declare no conflict of interest.
###### Abstract  
Defect inspection is an important issue in the field of industrial automation. In general, defect-inspection methods can be categorized into supervised and unsupervised methods. When supervised learning is applied to defect inspection, the large variation of defect patterns can make the data coverage incomplete for model training, which can introduce the problem of low detection accuracy. Therefore, this paper focuses on the construction of a defect-inspection system with an unsupervised learning model. Furthermore, few studies have focused on the analysis between the reconstruction error on the normal areas and the repair effect on the defective areas for unsupervised defect-inspection systems. Hence, this paper addresses this important issue. There are four main contributions to this paper. First, we compare the effects of SSIM (Structural Similarity Index Measure) and MSE (Mean Square Error) functions on the reconstruction error. Second, various kinds of Autoencoders are constructed by referring to the Inception architecture in GoogleNet and DEC (Deep Embedded Clustering) module. Third, two-stage model training is proposed to train the Autoencoder models. In the first stage, the Autoencoder models are trained to have basic image-reconstruction capabilities for the normal areas. In the second stage, the DEC algorithm is added to the training of the Autoencoder model to further strengthen feature discrimination and then increase the capability to repair defective areas. Fourth, the multi-thresholding image segmentation method is applied to improve the classification accuracy of normal and defect images. In this study, we focus on the defect inspection on the texture patterns. Therefore, we select the nanofiber image database and carpet and grid images in the MVTec database to conduct experiments. The experimental results show that the accuracy of classifying normal and defect patch nanofiber images is about 86% and the classification accuracy can approach 89% and 98% for carpet and grid datasets in the MVTec database, respectively. It is obvious that our proposed defect-inspection and classification system outperforms the methods in MVTec.  
defect inspection; autoencoder; SSIM; DEC clustering algorithm +
Footnote †: journal: Applied Sciences
# Adaptive Multi-Channel Residual Shrinkage Networks for the Diagnosis of Multi-Fault Gearbox
## 4 Experimental Validation and Analysis
### Case III  
(1) Mixed datasets: In order to further investigate the effectiveness of the proposed method in the diagnosis of mixed gear faults and bearing faults. As shown in Table 5, the mixed datasets contain six different mixed types of bearing faults and gear faults, including healthy bearing and healthy gear (HH), bearing with inner ring fault and 1-tooth worn gear (IWF), bearing with inner ring fault and 2 mm crack gear (ICF), bearing with outer ring fault and 1-tooth worn gear (OWF), bearing with outer ring fault and 2 mm crack gear (OCF), and bearing with ball fault and 3 mm crack gear (BCF). For consistency with the previous two cases, the datasets were collected at a motor speed of 3120 rpm with 2 V (Torque: 4.06 N\(\cdot\)m) load.  
(2) Analysis of Fault Diagnosis Results in Mixed Datasets: As shown in Figure 10, our proposed AMC-RSN still has better results compared to other methods. From Table 6, the proposed AMC-RSN can obtain the highest accuracy of 99.49% with the lowest standard deviation of 0.11%, which has better robustness compared to other networks. Compared with AMRS, MK-ResCNN, DRSN-CW, and ARSN-M, the proposed AMC-RSN achieves a slight advantage, while DRSN-CW takes the shortest time. Meanwhile, the CNN and MSC-MpResCNN also achieve higher accuracy rates. Therefore, the fault features of the mixed dataset are more prominent, while the proposed AMC-RSN can learn deeper features and classify them accurately more, which takes relatively more time.
## 6 Application of Algorithm to Various Scenarios for Fault Identification: Case Studies
### Effect of Fault Location  
The location of a fault on the network also affects the nature of faulty transients, which affects the performance of the protection scheme. Hence, the algorithm was tested for the two critical locations of faults on nodes 652 and 634.
## 3 Results and Discussion  
### Effect of Organic Concentration  
The effect of the different parameters affecting the preparation stage of the photocatalyst in the presence of methanol was previously discussed [42]. The influence of methanol concentration on the photocatalytic generation of hydrogen is herein explored. The main results are reported in Figure 2. The reaction rate observed during the photocatalytic runs at varying organic concentrations is properly described by a Langmuir-Hinshelwood model. The adsorbed species concentration may be thus calculated through the equilibrium reaction (1) and the Equation (1), in which K\({}_{\text{ads}}\) (M\({}^{-1}\)) is the adsorption equilibrium constant and \(\theta_{free}\) is the concentration of free active sites on the catalyst surface:  
\[\text{CH}_{3}\text{OH}+\theta_{free}\xleftrightarrow{\text{K}_{\text{ads}}} \text{CH}_{3}\text{OH}_{\text{ads}}\] (R1)  
\[\text{K}_{\text{ads}}=\frac{[\text{CH}_{3}\text{OH}_{\text{ads}}]}{\theta_{ free}[\text{CH}_{3}\text{OH}]} \tag{1}\]  
Figure 1: Schematic illustration of the annular batch reactor experimental set-up for the experiments conducted (**A**) under artificial UV and/or Visible light source and (**B**) under solar irradiation.  
Figure 2: Hydrogen evolution rate at different organic concentrations (methanol). Experimental conditions: V = 0.30 L; T = 35 \({}^{\circ}\)C; P = 1 atm; pH \(\approx\) 8.5; C\({}_{\text{(15\%wt)}}\)CuO/TiO\({}_{2}\) = 700 ppm.  
To obtain a value of the adsorption constant (Kads) for methanol over the Cu\({}_{2}\)O/TiO\({}_{2}\) composite photocatalyst, the following Langmuir-Hinshelwood-type model describing hydrogen generation rate is adopted (2):  
\[\text{r}_{\text{H}_{2}}=\text{k}^{\prime\prime}\frac{\text{K}_{\text{ads}}[ \text{C}\,\text{H}_{3}\,\text{OH}]}{1+\text{K}_{\text{ads}}[\text{C}\text{H}_{3} \text{OH}]} \tag{2}\]  
where k\({}^{\prime\prime}\) = k\({}^{\prime}\cdot\theta_{t}\) (k\({}^{\prime}\) being the kinetic constant) and K\({}_{\text{ads}}\) are the apparent kinetic constant of substrate oxidation and the organic adsorption constant, respectively. \(\theta_{t}\) (M) is the concentration of the total active site on the catalyst surface for a fixed catalyst load q(g/L). The term \(\theta_{t}\) is calculated through the following Equation (3):  
\[\theta_{t}=\text{N}\cdot\text{q} \tag{3}\]  
where N is the total moles of active sites per unit mass of catalyst (mol/g).  
Starting from Equation (3) and plotting the reciprocal of the hydrogen reaction rate versus the reciprocal of the organic concentration (Figure 3), a linear trend is observed, from which a suitable value for Kads is obtained. By following the optimization procedure reported elsewhere [44], a value of 0.22 M\({}^{-1}\) is found in presence of the photocatalyst tested when methanol is used as a scavenger. This value resulted in accordance with those reported in the literature for similar systems in presence of the same organic compound [45].
# C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
## 8 Limitations  
One limitation of this study is the use of a fixed number of clusters across all datasets, regardless of their size. While the datasets used for evaluation are comparable in size, it is evident that applying the same number of clusters to datasets with varying sizes may not yield optimal results. In future work, it would be beneficial to develop a dynamic method for determining the appropriate number of clusters based on the specific size and characteristics of each dataset.  
Another limitation of C-SHAP lies in its reliance on clustering, which, while improving computational efficiency, may obscure fine-grained feature interactions that are critical for certain datasets. For instance, in datasets with highly overlapping clusters, feature attribution might lose precision due to the aggregation process. Future studies could employ metrics like clustering fidelity or feature ranking stability to quantify these trade-offs and identify areas where interpretability is affected.  
Additionally, although C-SHAP was evaluated on a diverse set of well-known benchmark datasets, the size of these datasets remains relatively moderate. This raises concerns about the scalability of C-SHAP when applied to much larger datasets or those with a vast number of features. Also, the quality of the data and clusters can alter the performance of the proposed method. By taking data points from each cluster, there is a possibility that the effect of missing data may impact the accuracy of the method. Moreover, real-world datasets often contain noise, imbalances, or missing data, which can affect both clustering performance and feature attribution. Future evaluations should include such datasets to assess C-SHAP's robustness under diverse conditions and validate its generalizability across different domains.
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 3 Methodology
### Impacts of Single Satellite on the Accuracy and Integrity of Subset
#### 3.1.2 Impact of Single Satellite on the Integrity of Subset  
According to the slope-based RAIM algorithm mentioned above, the HPL is proportional to the max slope value of the subset. When a small HPL is required, the slope of each satellite in the subset should also be limited in a small range. Similarly, an increment is introduced to demonstrate the contribution of a single satellite on the integrity of the subset.  
\[\delta Slope_{i}=Slope_{\max,0}-Slope_{\max,i} \tag{16}\]  
The \(Slope_{\max,0}\) in Equation (16) is the max slope of the subset before the \(i\)-th satellite is introduced, and \(Slope_{\max,i}\) is the new max slope of the subset after that.  
If the newly introduced satellite always owns positive \(\delta Slope_{i}\) value, which means the max slope value is reduced in each iteration, then the HPL performance can keep improving as the subset size increases. However, this strategy may be obstructed as the abnormal tendency shown in Figure 2b when the added satellite not reduces but increases the slope of the former max slope satellite [42]. Fortunately, this abnormal tendency occurs in the satellite selection strategy mostly when the visible satellite number is very large and the subset size is close to the all-in-view subset thus is not so critical in this study.
# Scheduling Optimization of Mobile Emergency Vehicles Considering Dual Uncertainties
## 2 Model Construction
### Problem Description
In addition, the following assumptions are proposed to increase the degree of agreement between the model and the reality of mobile emergency response:  
**Assumption 1:**_The transportation capacity for supply distribution of all mobile emergency vehicles is homogeneous._  
**Assumption 2:**_Each mobile emergency vehicle can only serve one demand point within the upper limit of its capacity. The location of vehicles and demand points can be obtained by GPS or mobile phone._  
**Assumption 3:**_The resources of each mobile emergency response center can be freely complementary._
## 3 Results
### Spatial Distribution of LE in the Haihe River Basin of North China  
Figure 8 shows the comparison of multiyear (2013-2015) mean seasonality of LE estimates in Haihe River basin of China from GLASS, FLUXCOM, PML, GLEAM, BESS and MODIS LE products. LE showed large spatial variability over the whole area and strong seasonality according to the climate conditions and there was similar spatiotemporal variability of estimated terrestrial LE from the six LE products. In the spring (MAM), the estimated terrestrial LE values in the HRB ranged from 20 to 60 W/m\({}^{2}\) with a rapid rising in temperature. LE values in the southeast area were higher than that in the northern and northwestern area. In the summer (JJA), LE gradually increased relative to spring and reached the peak with higher temperatures and vegetation cover. The estimated terrestrial LE was mainly distributed in the range of 60 to 100 W/m\({}^{2}\), and the variability in the spatial distribution were similar to spring. During fall months (SON), LE decreased significantly relative to summer due to dropped temperature and reduced vegetation cover. The estimated terrestrial LE were mainly distributed in the range of 20 to 40 W/m\({}^{2}\), and the variability in the spatial distribution decreased relative to spring and summer. In the winter (DJF), the LE values were lowest and were obviously lower than that in the other seasons due to the low temperature and more snow. The estimated terrestrial LE values were below 20 W/m\({}^{2}\) in winter months and the spatial patterns of LE were similar to fall. However, for different satellite LE products, they exhibit an irregular pattern for the same seasons. This may be attributed to the different spatial resolutions, different model structures and different forcing inputs [36].  
To investigate the differences of the six satellite-based LE products, we further mapped spatial differences in the average annual terrestrial LE (2013-2015) between GLASS LE  
Figure 8: Multiyear (2013–2015) mean seasonality of LE in Haihe River basin of China. MAM: March, April and May; JJA: June, July and August; SON: September, October and November; DJF: December, January and February.  
product and other five LE products (Figure 9). One notices that the differences of the average annual terrestrial LE among the GLASS, FLUXCOM and PML LE products were less than 10 W/m\({}^{2}\) for most regions. In contrast, the differences of the average annual terrestrial LE among the GLASS, GLEAM, BESS and MODIS LE products were 10-20 W/m\({}^{2}\) for most regions. Overall, in comparison to GLASS LE product, GLEAM, BESS and MODIS LE products yielded lower average annual terrestrial LE across almost HRB areas.