esa-sceva/satcom-chunk-collection huggingface dataset:
# MCIDN: Deblurring Network for Metal Corrosion Images
###### Abstract  
The analysis of corrosion images is crucial in materials science, where acquiring clear images is fundamental for subsequent analysis. The goal of deblurring metal corrosion images is to reconstruct clear images from degraded ones. To the best of our knowledge, this study introduces the first paired blurry-sharp image dataset specifically designed for the metal corrosion domain, filling a critical gap in the existing research. This innovative approach effectively addresses the unique challenges associated with deblurring metal corrosion images. We propose a novel metal corrosion images deblurring network (MCIDN) that employs a dual-domain attention mechanism, integrating both spatial and frequency domains to enhance image clarity. This innovative approach effectively addresses the unique challenges associated with deblurring metal corrosion images. While self-attention is widely used in visual tasks, its quadratic complexity often leads to high computational costs. To address this issue, we introduce a new spatial channel attention module (SCAM) that employs dynamic group convolutions to achieve self-attention, effectively integrating information from local regions and enhancing representation learning capabilities. Recognizing the critical role of frequency components in image restoration, we develop a frequency channel attention module (FCAM) that selectively focuses on different frequency components of images, thereby enhancing deblurring performance. These two attention modules are seamlessly integrated into our network. Compared to existing methods, our approach demonstrates superior performance on datasets of blurry metal corrosion images, achieving a peak signal-to-noise ratio (PSNR) of 32.8645 dB and a structural similarity (SSIM) of 0.9768. These metrics indicate that our method provides clearer and more detailed reconstructions, significantly enhancing the image quality.  
metal corrosion; image deblurring dual-domain learning; deep learning +
Footnote †: journal: Applied sciences  
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## 3 Materials and Methods
### The IoT Monitoring Platform  
The IoT monitoring platform is composed of two main blocks: the ICON OneM2M-compliant platform by TIM [34] and the application platform by Sistematica called \"Maritime mIoT Service Framework\" (MSF).  
The former is a <<store & share>> platform, implementing resource-oriented RESTful APIs compliant with the OneM2M standard. It is installed by TIM in a data center HW/SW environment to ensure 24 h availability in field trials and research projects.  
The latter enables the 5 GT services for maritime transport operators, decoupling user services from the underlying OneM2M platform and providing user-defined service with a unique and straightforward interface, aggregating data from other sources. For example, in addition to container data taken from ICON, the MSF applications also aggregate ship location data from the Smart mIoT Gateway GNSS and weather data from external services. An instance of the MSF is installed both in the cloud for users on the ground and in the Smart mIoT Gateway onboard to offer services to the crew as well. The data feeding the onboard applications are constantly updated against the platform for ground users via a OneM2M standard-compliant MQTT broker. Services for users are presented by a web app upon authenticated access by the identity manager. An example of the app access page is shown in Figure 9. The \"Ship Monitoring\" screen section shows the owner's ship on the map. For each ship, the associated information is shown: ship name, IMO, location and relative age. In addition, a list of the embarked containers is displayed using the button \"Show Containers\":  
* The \"Vessel management\" function allows for adding new ships to the 5 GT system for the registered shipowner, vessels to be deleted, and containers to be loaded and unloaded for each shipload.
* Selecting the \"Alarm List\" button or the \"Alarms\" button, the list of alarms is displayed. Each alarm has the following parameters: the ship, the container ID, the alarmed sensor, a description and a flag for the deleted alarm.
* The \"Statistics\" tab allows two graphs to be displayed: a doughnut with the amount of containers without alarms in green and with alarms in red, and another one that specifies the type of alarm using different colors (e.g., yellow for open door, blue for mechanical bump, etc.).  
Figure 9: Web app for the users of the Maritime mIoT Service Framework.
# Improved Bilateral Filtering for a Gaussian Pyramid Structure-Based Image Enhancement Algorithm
## 3 Improved Bilateral Filtering Function
### Improved Selection of Pixel Difference Scale Parameters  
In the \(\Omega_{p,i,j}\) point set, which is used as a sample to find the standard deviation (\(\sigma_{n}\)) of pixel intensities in an image, the gray value of each pixel is defined as \([a_{(2p-1,2p-1)},a_{(2p-1,2p)}\dots a_{(2p+1,2p+1)}]\). However, unlike with the spatial domain, selection of this standard deviation as the scale parameter leads to an excessively large value for \(\sigma_{d}\), as shown in [24]. Hence, a rotating window function for determining the value of \(\sigma_{d}\) is proposed as follows. A \((2p+1)\times(p+1)\) rectangular window centered on the \((x,y)\) pixel is selected, with an offset angle defined as \(2\pi/K\). The standard deviation of pixels in this rectangular window is then calculated, following which the window is rotated by the offset angle. This calculation and rotation process is successively iterated for a complete revolution (i.e., \(K\) rotations should be completed). Finally, the minimum pixel standard deviation \(\min(\sigma_{n})\) computed is selected as \(\sigma_{d}\); the window in which this value was calculated is used in subsequent filtering. \(\sigma_{d}\) is defined as follows:  
\[\sigma_{d}=\min(\sigma_{n})=\min_{k=0,1,\dots,K-1}\{std[f(x,y)_{2\pi k/K}]\} \tag{10}\]  
where \(f(x,y)_{2\pi k/K}\) is the value of a pixel at a given location in the window, \(K=8\).
# Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running
## 4 Discussion
### Limitations  
There were a few limitations to the current study. Firstly, six participants had missing data due to either faulty units or the poor connection to the satellites. The current sample size of 13 participants was smaller than expected since the experiment was halted prematurely due to the COVID-19 pandemic. A larger sample size may have brought about more reliable results, which was unfortunately not possible due to time constraints. Secondly, environmental factors (e.g., presence of clouds) during the experiment may have occurred and affected the results. Thirdly, we acknowledge that the use of smartphone cameras can reduce the accuracy of data collected due to optical effects, such as lens distortion and parallax error. For fast sprint movements, the relatively low frame rate of 60 Hz could have also compromised accuracy of speed and time data collected. Lastly, the current study investigated only two variables of linear sprints (total distance and peak speed). In the future, researchers should expand to other variables and movement types concerning the utilizations of GNSS units in sports such as change in direction, acceleration, and deceleration.
# A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI
## 3 Experimental Results and Discussion
### Performance Evaluation Using Different Atlas  
Mean performance metrics for different network configurations by fine-tuning using the atlases mentioned in Section 2.2.1 are represented in the following subsections.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Autism Brain Imaging Data Exchange (ABIDE) Dataset**} \\ \hline \multirow{2}{*}{**Site**} & \multicolumn{2}{c}{**Count**} & \multirow{2}{*}{**Age Range**} \\ \cline{2-2} \cline{4-5}  & **ASD** & **Control** & & **Total** \\ \hline Caltech & 5 & 10 & 15 & 17–56 \\ CMU & 6 & 4 & 10 & 19–40 \\ KKI & 12 & 20 & 32 & 8–13 \\ LEVEN & 26 & 30 & 56 & 12-32 \\ MAX\_MUN & 19 & 27 & 46 & 7–58 \\ NYU & 74 & 98 & 172 & 6–39 \\ OHSU & 12 & 13 & 25 & 8–15 \\ OLIN & 14 & 14 & 28 & 10–24 \\ PITT & 24 & 26 & 50 & 9–35 \\ SBI & 12 & 14 & 26 & 20–64 \\ SDSU & 8 & 18 & 26 & 9–17 \\ Stanford & 12 & 13 & 25 & 8–13 \\ Trinity & 19 & 25 & 44 & 12–26 \\ UCLA & 48 & 37 & 85 & 8–18 \\ UM & 46 & 73 & 119 & 8–29 \\ USM & 43 & 24 & 67 & 9–50 \\ YALE & 22 & 18 & 40 & 8–18 \\ TOTAL & 402 & 464 & 866 & 6–64 \\ \hline \hline \end{tabular}  
* Detailed information about ABIDE dataset is available in [42].  
\end{table}
Table 1: Phenotypic information summary of the participants from the ABIDE dataset.  
Figure 6: Data partitioning using stratified 5-fold cross-validation.  
Tables 2-5 represent the value of sensitivity, F1-score, and AUC score for each atlas. Values of mean accuracy and its standard deviation are also shown in percentages. A dropout probability of 0.8 was introduced between each layer to control overfitting, as shown in Figure 5. All the models were compiled using the Adam optimizer with a learning rate of 0.0001 and binary cross-entropy loss function and trained with a batch size of 10. Confusion matrix and mean AUC (area under receiver operating characteristic curve) curves were also represented in each case.
# Consensus Big Data Clustering for Bayesian Mixture Models
## 2 Related Work
#### Preliminaries  
Dirichlet processes (DPs) are a family of stochastic processes. A Dirichlet process defines a distribution over probability measures \(G:\Theta\rightarrow\mathbb{R}^{+}\), where for any finite partition of \(\Theta\), say \(\left\{\theta_{k}\right\}_{k=1}^{K}\), the random vector  
\[(G(\theta_{1}),G(\theta_{2})\ldots G(\theta_{K})) \tag{1}\]  
is jointly generalized under a Dirichlet Distribution (\(G(\theta)\) is a random variable since \(G\) itself is a random measure and is sampled from the Dirichlet process), written as  
\[(G(\theta_{i}),G(\theta_{2})\ldots G(\theta_{K}))\sim\text{Dir}(\pi G_{0}( \theta_{1}),\pi G_{0}(\theta_{2})\ldots\pi G_{0}(\theta_{K})) \tag{2}\]  
where \(\alpha\) is called the concentration parameter and \(G_{0}\) is the base distribution; \(\alpha G_{0}\) collectively is called the base measure.  
Dirichlet processes are really useful in the task of nonparametric clustering via using a mixture of Dirichlet processes (commonly DP mixture models or Infinite mixture models). In fact, a DP mixture model can be seen as an extension of Gaussian mixture models over a nonparametric setting. The basic DP mixture model follows the following generative story:  
\[\begin{array}{c}\text{Likelihood:}\ \mathbf{y}_{n}\mid\theta_{n} \sim F(\mathbf{y}_{n}\mid\theta_{n})\\ \text{Conditional Prior:}\ \theta_{n}\mid G\sim G\\ \text{Hyperparameter:}\ G\sim\text{DP}(G_{0},\alpha)\end{array}\]  
where \(G\sim\text{DP}\) denotes sampling from a Dirichlet process given a base measure.  
When we are dealing with DP mixture models for clustering, it helps to integrate out \(G\) with respect to the prior on \(G\)[53]. Therefore, we can write the clustering problem in an alternate representation, although the underlying model remains the same.  
Likelihood: \(\mathbf{y}_{n}\mid c_{n},\Phi\sim F(\mathbf{y}_{n}\mid\phi_{cn})\)  
Latent Distribution: \(c_{n}\mid\mathbf{p}\sim\text{Discrete }(p_{1},p_{2}\cdots p_{K})\)  
Priors: \(\phi_{k}\sim G_{0}\mathbf{p}\sim\text{Dir}(p_{1},p_{2}\ldots p_{K})\)  
where \(c_{n}\) is the cluster assignment for the \(n^{\text{th}}\) point and \(\Phi=\{\phi_{k}\}_{k=1}^{K}\) are the likelihood parameters for each cluster. \(K\) denotes the number of clusters, and being a nonparametric model, we assume \(K\rightarrow\infty\).  
If the likelihood and the base distribution are conjugate, we can easily derive a posterior representation for the cluster assignments or the latent classes and use inference techniques such as mean-field VB and Markov chain Monte Carlo. The work [53] also describes various inference methods in the case of a non-conjugate base distribution.  
Dirichlet processes are extremely useful for clustering purposes as they do not assume an inherent base distribution, and therefore it is possible to apply Dirichlet process priors over complex models.
## 4 Discussion
### Limitations  
This study has some limitations that should be considered. First, only men were recruited, and the sample size of participants was small. In addition, only the acute effects of self-selected music on cortical activation and anaerobic performance were examined. Therefore, future studies with larger sample sizes and implementation of longer term interventions are needed to examine and confirm the observations made in this study and to explore the long-term effects of self-selected music intervention. Second, we did not record endocrinological parameters, which could have contributed to elucidating our findings better. Third, the study relied on individuals' self-selected music during the warm-up. This introduced variability in music preferences, genres, and tempo, which may influence the psychological and physiological responses differently. Standardizing the music selection process or categorizing participants based on their musical preferences could provide more consistent and reliable results. Furthermore, [PERSON] et al. [53] showed that volume may affect the perception of music during exercise. The volume of music in this study was controlled by the participants themselves, rather than set to a fixed level to fit their habits of listening to music during exercise, which may have had some effect on the results. Finally, although subjects with more than one year of training experience were not intentionally selected during the recruitment process, the average training experience of subjects in this study was 3.65 years and the level of training experience may influence the results.
## 2 Materials and Methods
### Flux Data  
Flux data provide continuous net ecosystem exchange (NEE) measurements, assisting with the mechanistic understanding of temporal and spatial variations of C fluxes [17,28]. Fluxes of momentum, sensible heat, water vapor, and CO\({}_{2}\) were measured using the eddy covariance (EC) technique [26]. Velocity components, air temperature, water vapor density, and CO\({}_{2}\) concentration in the air were sampled rapidly, and calculations of relevant covariances were performed from these samples to obtain the fluxes. Wind speed and CO\({}_{2}\) mixing ratio were measured at a specific height above the ground (_i.e._, 39 m for SK-OA and 25 m for SK-OBS) using a three dimensional sonic anemometer-thermometer (SAT, Model R3, Gill Instruments, Lymington, UK) and a closed-path infrared gas analyzer (IRGA) (Model LI-6262), respectively.  
A standard procedure implemented by Fluxnet-Canada was used to estimate the annual NEP for model evaluation. NEP was partitioned into GPP and \(R_{e}\) using gap-filled half-hourly measurements of NEE. Empirical regressions of nighttime NEE _versus_ temperature and daytime GPP _versus_ photosynthetic active radiation (PAR) were used to estimate \(R_{e}\) and GPP, respectively, and gaps were filled as described by [PERSON] _et al._[26]. Three steps were followed to acquire the annual NEP, GPP, and \(R_{e}\). First, the net ecosystem exchange (NEE) was estimated as the sum of the measured eddy and air-column storage fluxes with two adjustments. The first is a correction for low turbulence, where a critical value of friction velocity (u*) is used to filter unacceptable data. Typically values of u* \(>\) 0.35 m s\({}^{-1}\) were employed, with individual values determined for each site. The second adjustment was for poor energy-balance-closure, where turbulent fluxes (including NEP) were adjusted so that the sum of sensible and latent heat fluxes balanced the net radiation minus energy storage terms. Next, measured \(R_{e}\) was estimated as \(R_{e}=-\)NEP during periods when GPP was known to be zero, specifically at night and during both night and day in the cold season (periods when both air (\(T_{a}\)) and 2-cm soil (\(T_{s}\)) temperatures were below 0 \(\ \lx@math@degree\)C). Finally, GPP was estimated as NEP + \(R_{e}\) (daytime) or zero (nighttime and during periods when both \(T_{a}\) and \(T_{s}\) were less than 0 \(\ \lx@math@degree\)C) with an additional empirical model to fit the non-zero GPP data for the entire year.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
###### Abstract  
The submerged membrane filtration concept is well-established for low-pressure microfiltration (MF) and ultrafiltration (UF) applications in the water industry, and has become a mainstream technology for surface-water treatment, pretreatment prior to reverse osmosis (RO), and membrane bioreactors (MBRs). Compared to submerged flat sheet (FS) membranes, submerged hollow fiber (HF) membranes are more common due to their advantages of higher packing density, the ability to induce movement by mechanisms such as bubbling, and the feasibility of backwashing. In view of the importance of submerged HF processes, this review aims to provide a comprehensive landscape of the current state-of-the-art systems, to serve as a guide for further improvements in submerged HF membranes and their applications. The topics covered include recent developments in submerged hollow fiber membrane systems, the challenges and developments in fouling-control methods, and treatment protocols for membrane permeability recovery. The highlighted research opportunities include optimizing the various means to manipulate the hydrodynamics for fouling mitigation, developing online monitoring devices, and extending the submerged HF concept beyond filtration.  
submerged hollow fiber membranes; water treatment; fouling mitigation; critical flux; module design +
Footnote †: journal: Applied Sciences
## 3 Results  
### Initial Concentration of Cytokines in the Supernatants  
The indirect influence of electric stimulation was analyzed using PBMCs and pre-osteoblasts. The supernatants of electrically stimulated osteoblasts (ESO) and non-stimulated osteoblasts (NSO) were collected after 7 d, 14 d, and 21 d, pooled, and used for the stimulation. The supernatants were diluted 1:1 with RPMI, as described in Section 2.3.  
The concentration of possible mediators released by human osteoblasts that might affect the PBMCs and pre-osteoclasts was analyzed before incubating the diluted supernatant with the cells. The protein concentrations of BMP-2, cystatin C, IFN-\(\beta\), IL-10, IL-1\(\beta\), IL-6, OPN, TGF-\(\beta\)1, and TNF-\(\alpha\) were determined via LEGENDplex(tm) multiplex assay. No signal was detectable for BMP-2, cystatin C, IL-1\(\beta\), and OPN. The concentrations for TNF-\(\alpha\), IL-6, IL-10, IFN-\(\beta\), and TGF-\(\beta\)1 are shown in Table 2. The highest concentration of TNF-\(\alpha\) was detectable with the ESO for all time points: about twice as high as with the NSO. The amount of IL-6 rose in the ESO supernatants constant over time. The IL-6 concentration for the NSO peaked at 14 d. IL-10, IFN-\(\beta\), and TGF-\(\beta\)1 were not detectable for all time points. IL-10 was just determined for 14 d ESO, 7 d NSO, and 21 d NSO. IFN-\(\beta\) was determined for ESO only at 14 d. The IFN-\(\beta\) concentration in the control varied between the stimulation times, with the highest amount at 21 d. The TGF-\(\beta\)1 concentration was only measurable in HEF after 21 d whereas, for the control, it was also measurable at 7 d.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & & **ESO** & & & **NSO** & \\ \hline
**Time [d]** & 7 & 14 & 21 & 7 & 14 & 21 \\ \hline \multicolumn{8}{c}{**Pro-osteoclastic**} \\ \hline
**TNF-\(\alpha\) [pg/mg]** & 6.00 & 6.20 & 5.70 & 2.56 & 3.27 & 2.55 \\ \hline \multicolumn{8}{c}{**Anti-osteoclastic**} \\ \hline
**IL-10 [pg/mg]** & 0.00 & 0.92 & 0.00 & 0.96 & 0.00 & 1.47 \\
**IFN-\(\beta\) [pg/mg]** & 0.00 & 1.48 & 0.00 & 1.3 & 0.76 & 2.36 \\ \hline \multicolumn{8}{c}{**Bone remodeling**} \\ \hline
**TGF-\(\beta\)1 [pg/mg]** & 0.00 & 0.00 & 0.59 & 0.69 & 0.00 & 0.62 \\ \hline
**IL-6 [pg/mg]** & 176.45 & 213.29 & 387.33 & 231.20 & 318.82 & 278.55 \\ \hline \end{tabular}
\end{table}
Table 2: Protein concentration of the pooled supernatants of electrically stimulated osteoblasts (ESO) and non-stimulated osteoblasts (NSO) after 7, 14, and 21 days.
## 3 Results and Discussion
### Effect of Friction Coefficient  
The friction coefficient, \(\mu\), plays an important role in the bearing capacity of piles, especially for friction piles. The empirical value of friction coefficient has been mentioned above. In the absence of test data, a simple method is often used to determine the friction coefficient, \(\mu\) = \(tan\delta\). Generally, the friction angle of the pile-soil interface, \(\delta\), ranges from 9\({}^{\circ}\) to 21\({}^{\circ}\), which results in \(\mu\) ranging from 0.15 to 0.4. To investigate the effect of the friction coefficient on skin friction and the axial load of the pile, \(\mu\) = 0.05, 0.1, 0.2, 0.3, 0.4 were selected to analyze. The distribution of skin friction and axial load versus normally depth are plotted in Figure 7a,b, respectively.  
Figure 6: Effect of consolidation time on (**a**) skin friction and (**b**) axial load.  
Figure 7: Effect of friction coefficient on (**a**) skin friction (**b**) and axial load.  
Figure 7a shows that with an increase in friction coefficient, the NSF and PSF increased, and the location of neutral point decreased. As expected in the ESM above, with increases in the friction coefficient, the slope of fully mobilized NSF versus depth decreased. It also shows the distribution of skin friction of theoretical calculation by ESM for \(\beta\) = 0.1 and \(\beta\) = 0.25, with a red dash line, considering the surcharge in the soil surface. Comparing to the results of the FEM, fully mobilized NSF matches the theoretical value. However, there is a deviation in the range of fully mobilized PSF, and the larger the friction coefficient, the greater the deviation. This may be due to the fact that the deformation of soil is not linear, and the relative displacement of pile and soil near the pile bottom was smaller than the theoretical hypothesis. The larger the friction coefficient is, the more significant the non-linear deformation of the soil below the neutral point is.  
Figure 7b shows that with an increase in the friction coefficient, the maximum axial load of pile increased. The theoretical values of the corresponding axial forces for \(\beta\) = 0.1 and \(\beta\) = 0.25 are also shown. The bigger the friction coefficient is, the larger the deviation between ESM value and FEM value is. The reasons are described above. It is well known that the larger the friction coefficient is, the greater the settlement constraint of pile on soil is, and the greater the drag-load is in the same consolidation conditions. The normalized position of the neutral point, \(r/L\), and the normalized axial load, \(F_{max}/p\), varied with friction coefficient are plotted in Figure 8, where the best fitting equations are also given.