esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Microbiome Influence on Atopic Dermatitis
### Atopic Dermatitis  
AD, also known as atopic eczema, is a chronic inflammatory skin disease characterized by dry and red skin, intense itching, and recurrent scalp lesions with a typical distribution [58; 115; 116; 117; 118]. The pathogenesis of the disease is not fully understood; however, it appears to result from a complex interaction between defects in the skin barrier function, environmental and infectious agents, and immune system dysregulation [119]. Although it mostly begins in childhood, it is also prevalent in adults, being the leading non-fatal health problem attributed to skin diseases, inflicting a significant psychosocial weight on patients and their families, elevating the likelihood of conditions such as food allergies, asthma, allergic rhinitis, and other immune-related inflammatory ailments, along with mental health issues [120]. Therefore, it carries a relevant weight in healthcare systems [115]. AD is classified as mixed or pure. Mixed AD represents approximately 60% of cases and is associated with respiratory symptoms. On the other hand, pure AD is classified when there is no associated respiratory disease [121].  
The symptoms of AD differ depending on the age group. Infants commonly experience AD symptoms on the scalp, face, neck, trunk, and the outer parts of the limbs. In children, the areas affected usually include the inner parts of the limbs such as the elbows, knees, neck, wrists, and ankles. During adolescence and adulthood, the inner parts of the limbs, hands, and feet are frequently more affected. Regardless of age, the itchiness associated with AD typically persists throughout the day and intensities at night, causing sleep deprivation and significantly reducing the quality of life [120].  
In all stages of the disease, the lesions can present as acute, subacute, or chronic eczemous. These stages can occur sequentially, although in most cases, they coexist in different regions of the body or even in the same region, indicating the characteristic episodic evolution of the condition [121].  
Due to similarities in some symptoms, it can sometimes be challenging to differentiate AD from other skin conditions (such as seborrheic dermatitis, contact dermatitis, psoriasis, or scabies). Nevertheless, in many instances, the diagnosis is aided by the presence of atopy in the family history and the pattern of lesion distribution [119].
## 4 Experiment and Analysis
### Outdoor Posture Estimation Experiment  
Table 9 presents the absolute trajectory errors for the KITTI dataset comparison between the algorithm presented in this paper and DS-SLAM, DynaSLAM. Table 9 shows that the algorithm used in this paper performs better in terms of root-mean-square error than the other algorithms in the 01 and 02 sequences, and that DS-SLAM and DynaSLAM perform similarly in these two sequences. In the 03 sequence, DynaSLAM performs poorly while DS-SLAM performs best in terms of standard deviation and root-mean-square error. Overall, this paper's errors in various outdoor dataset sequences outperform comparable algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**DS-SLAM (m)**} & \multicolumn{4}{c}{**DynaSLAM (m)**} & \multicolumn{4}{c}{**Our-SLAM (m)**} \\ \cline{2-10}  & **RMSE** & **MEAN** & **SD** & **RMSE** & **MEAN** & **SD** & **RMSE** & **MEAN** & **SD** \\ \hline walking\_xyz & 0.1365 & 0.0681 & 0.0369 & 0.1087 & 0.0374 & 0.0426 & 0.1056 & 0.0324 & 0.0456 \\ walking\_static & 0.0451 & 0.0652 & 0.0371 & 0.0315 & 0.0368 & 0.0366 & 0.0326 & 0.0231 & 0.0271 \\ walking\_halfsphere & 0.1026 & 0.0892 & 0.0510 & 0.0786 & 0.0350 & 0.0291 & 0.0821 & 0.0298 & 0.0634 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of absolute trajectory errors of different algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**ORB-SLAM3 (ms)**} & \multicolumn{2}{c}{**DS-SLAM (ms)**} & \multicolumn{2}{c}{**DynaSLAM (ms)**} & \multicolumn{2}{c}{**Our-SLAM (ms)**} \\ \cline{2-9}  & **Median** & **MEAN** & **Median** & **MEAN** & **Median** & **MEAN** & **Median** & **MEAN** \\ \hline walking\_xyz & 24.6 & 26.8 & 28.8 & 30.6 & 27.5 & 30.1 & 28.9 & 29.5 \\ walking\_static & 24.5 & 26.7 & 28.4 & 31.2 & 28.0 & 30.5 & 28.4 & 29.2 \\ walking\_halfsphere & 24.5 & 26.4 & 28.8 & 31.5 & 27.1 & 31.5 & 29.0 & 30.2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Tracking time comparison.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**ORB-SLAM3 (fps)**} & \multicolumn{2}{c}{**DS-SLAM (fps)**} & \multicolumn{2}{c}{**DynaSLAM (fps)**} & \multicolumn{2}{c}{**Our-SLAM (fps)**} \\ \hline walking\_xyz & 37.5 & 31.2 & 32.5 & 33.7 \\ walking\_static & 37.7 & 32.5 & 31.6 & 34.5 \\ walking\_halfsphere & 37.6 & 32.2 & 32.8 & 33.2 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Frame rate comparison.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**DS-SLAM (m)**} & \multicolumn{4}{c}{**DynaSLAM (m)**} & \multicolumn{4}{c}{**Our-SLAM (m)**} \\ \cline{2-10}  & **RMSE** & **MEAN** & **SD** & **RMSE** & **MEAN** & **SD** & **RMSE** & **MEAN** & **SD** \\ \hline
01 & 5.263 & 4.268 & 4.561 & 4.325 & 3.685 & 4.254 & 2.256 & 2.037 & 1.562 \\
02 & 0.855 & 0.971 & 0.928 & 0.978 & 0.869 & 0.965 & 0.962 & 0.798 & 0.668 \\
03 & 0.036 & 0.057 & 0.068 & 0.156 & 0.085 & 0.093 & 0.086 & 0.065 & 0.075 \\
# Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture
## 3 Proposed IoT-Enabled Healthcare Architecture  
The proposed IoT-enabled healthcare architecture consists of three components responsible for the three main functions: communication, management, and prioritization. The communicator component achieves communication between heterogeneous and homogenous healthcare devices, whether they are passive or active. The manager component accomplishes administration, adaptation, and management functions. The prioritizer component is responsible for classifying health data into several levels of importance. Each component and how the proposed healthcare architecture works are described below. Furthermore, a mathematical analysis is introduced. Moreover, for more clarification of the proposed healthcare architecture, a simple use case is demonstrated.
## 3 Results
### Expression Profile of Muscle-Specific Factors and MicroRNAs in the EUs
#### 3.2.1 Expression Profile of Muscle-Specific Factors  
We analyzed the expression of the muscle-specific factors, _PAX7_, _MYOD1_ and _MYOG_ in huMPCs cultured on plates or in EUs under standard gravity and in proliferation or differentiation conditions by qRT-PCR. We found that the selected genes showed a similar expression trend on plates and in EUs (Figure 4). In particular _PAX7_, a gene characterizing quiescent satellite cells and proliferating myoblasts, and _MYOD1_, a gene implicated in the proliferative boost required to commit and sustain differentiation, appeared similarly expressed in huMPCs cultured on plates and in EUs. _MYOD1_ also appeared upregulated and similarly expressed on plates and in EUs in differentiation conditions. _MYOG_, a marker of myogenic terminal differentiation, was found upregulated in huMPCs cultured in DM, as expected, both on plates and in EUs (Figure 4). These data suggested that the use of the EU system does not affect the physiological expression of muscle-specific factors in these cells.  
\begin{table}
\begin{tabular}{c c c} \hline
**RNA Isolation Kit** & \
\begin{tabular}{c} **Yield on Plates** \\ **ng/\(\mu\)L (260/280 Ratio)** \\ \end{tabular} &
\begin{tabular}{c} **Yield inside the EUs** \\ **ng/\(\mu\)L (260/280 Ratio)** \\ \end{tabular} \\ \hline mirVana miRNA & total RNA 18.6 (2.05) & total RNA 29.5 (1.54) \\ \hline PureLink miRNA & microRNAs 3.0 (1.96) & microRNAs 3.2 (1.90) \\ PureLink RNA Mini Kit & total RNA 16.8 (2.06) & total RNA 18.9 (2.13) \\ \hline miRNeasy Micro Kit & total RNA 72.5 (1.98) & total RNA 78.1 (1.90) \\ \hline \end{tabular}
\end{table}
Table 4: RNA quantification from single ThermanoxTM coverslip cultured in plates or EUs. Total RNA or microRNAs isolated using the indicated kits from huMPCs seeded on ThermanoxTM coverslips and cultured either on plates or inside the EUs. Quantification was performed by a NanoDropTM spectrophotometer. The average 260/280 ratios are reported.  
\begin{table}
\begin{tabular}{c c c} \hline
**Conditions** & **Plate** & **EU** \\ \hline PDL & 4.0 \(\pm\) 0.2 & 3.8 \(\pm\) 0.1 \\ \hline FI (\%) & 55.2 \(\pm\) 5.1 & 57.0 \(\pm\) 2.3 \\ \hline \end{tabular}
\end{table}
Table 3: Population doubling level (PDL) and fusion index (FI). The proliferation rate was calculated as the PDL after 10 days of culture. The ability to fuse was calculated as FI (%) after 7 days of differentiation.
# Smart Factory Web--A Blueprint Architecture for Open Marketplaces for Industrial Production
## 5 Smart Factory Web Approach
### Search for Production Capabilities
#### 5.4.3 Search Result  
Figure 11 shows the search result specification in Smart Factory Web. The search result is based on the data model defined in Figure 5. The search returns a set of supply chains as a result. Supply chains consist of one or more assets. An asset is a factory or part of the factory equipment hierarchy, as defined in [17]. In the context of SFW, an asset can be a supplier for another factory. In this section, the focus was on the search for factories and factory combinations (supply chains), and for this reason an asset is considered as a factory.  
The search result returns the factories (assets) of a supply chain, the capabilities of the individual factories, the processed input products, the generated output products, precise property descriptions of the products and the matching capabilities. Data objects of the search result are:  
* Asset data: unique ID, name, semanticID, description, capabilities;
* Capability data: unique ID, name, semanticID, description, properties, products (input/output);
* Product (input/output) data: unique ID, name, semanticID, description, properties;
* Property data: unique ID, name, semanticID, value or value range (minValue, maxValue), unit of measure.  
Figure 10: Merged factory combinations.  
Figure 12 shows an abstract example of a search result in SFW. The possible result data (return values) of the search are defined in the search result specification (Figure 11). The search result in Figure 12 shows two supply chains.  
* The first supply chain consists of three factories (Factory 1-3) to fulfill the required capabilities (Cap1, Cap2, Cap3, Cap4) together. Factory 2 executes two production steps (Cap2, Cap3). Factory 1 and 3 execute one production step.  
Figure 11: Search result specification.  
Figure 12: Search result—search for electronics manufacturing.  
* The second supply chain consists of four factories (Factory 1-4) to fulfill the required capabilities (Cap1, Cap2, Cap3, Cap4) together. Each factory executes one production step.
## 3 Results
### Moderator Analysis  
The results of a moderator analysis of OSE versus CSE are summarized in Table 4. To analyze the influence of potential moderator variables on overall cognitive function, and considering that only a small number of intervention studies were available, we only analyzed the moderator variables of the 15 cross-sectional studies according to age group. These groups included children aged 5-16 years, young adults aged 17-35 years, and elderly aged \(>\)56 years. Our goal was to explore whether OSE and CSE influence the overall cognitive function of different age groups. Based on our selection criteria, we had one study of children, eight studies of young adults, and six studies of the elderly to evaluate. Consequently, we only compared differences for the younger and older adults. The effect size for OSE versus CSE on overall cognition in the young adults was 0.384 (95% CI (\(-\)0.097, 1.213); \(p=0.002\)) (Table 4), and no significant heterogeneity was observed across these studies (Q(7) = 8.129, \(p>0.05\), I\({}^{2}=13.89\%\)). Meanwhile, the effect size for OSE versus CSE on overall cognition in the older adults was 0.197 (95% CI (0.033, 0.923); \(p=0.105\)) (Table 4). There was no significant heterogeneity observed between these studies either (Q(5) = 6.077, \(p>0.05\), I\({}^{2}=17.72\%\)). Taken together, these results demonstrate that the cognitive benefits of OSE are greater for younger adults.  
According to a previously described study quality assessment tool [38; 39; 53], each study was graded. Scores for the 15 cross-sectional studies ranged from 7 to 9. A score of 9 was considered to indicate high quality, 8 indicated moderate quality, and 7 indicated low quality(Detailed grading rules and scores for each article can be obtained in the Supplementary Materials). Differences in the effects of  
Figure 3: Forest plot for the efficacy of OSE compared to CSE in intervention studies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subfunction** & **Number of Studies** & **Effect Size** & **95\% CI** & \(p\)**-Value** \\ \hline Inhibition & 7 & 0.247 & \(-\)0.173, 1.213 & 0.042 \\ \hline Cognitive flexibility & 5 & 0.360 & 0.036, 0.923 & 0.013 \\ \hline Visuospatial attention & 3 & 0.209 & 0.040, 0.359 & 0.314 \\ \hline Processing speed & 2 & 0.103 & 0.098, 0.108 & 0.657 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Conditions of specific performance.  
OSE and CSE on cognitive function were found to be significant in both the high- and moderate-quality studies and were not significant in the low-quality articles.
# A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT Devices
## 4 Result Analysis
### Training on MNIST Dataset  
Table 4 lists the traditional performance metrics for training an image classifier on the MNIST dataset using the PyTorch and Libtorch libraries.  
Figure 4: Comparison graph plots for FashionMNIST dataset.  
Training on the MNIST dataset using PyTorch and Libtorch takes less time compared to the FashionMNIST experiments, mainly because of the simplicity of this dataset. In terms of the model's performance, Libtorch outperforms PyTorch by a small magnitude. Furthermore, the device memory consumption and average temperature during the training are lower for Libtorch. However, Libtorch requires slightly longer to train.  
In summary, while PyTorch provides an easier approach for development as it abstracts most of the inner logic from the developers, Libtorch may be preferable for scenarios prioritizing more reliable performance and sustainability due to its lower resource consumption and energy usage. As evidenced by Figure 5, the running code on Python significantly increases CPU and RAM utilization, directly affecting the power draw in the process. A smaller power footprint despite the longer duration of training means there may be more avenues for lowering the carbon footprint. Nevertheless, Libtorch implementation is not as widely adapted as Pytorch and requires the handling of more complex coding compared to PyTorch. The choice between these frameworks would depend on the specific trade-offs between performance accuracy and operational efficiency relevant to the deployment context.  
\begin{table}
\begin{tabular}{l l l} \hline
**Metrics** & **PyTorch** & **Libtorch** \\ \hline Accuracy & 98.53\% & **98.80\%** \\ \hline Precision & 98.52\% & **98.73\%** \\ \hline Recall & 98.51\% & **98.79\%** \\ \hline F1-Score & 98.51\% & **98.75\%** \\ \hline Training time & **1.20 h** & 1.29 h \\ \hline Total Power & 0.21 kW & **0.14 kW** \\ \hline Average Temperature & **76.3**\({}^{\circ}\)C & 78.5 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 4: Model performance on MNIST dataset. The best results are given in bold.  
Figure 5: Comparison graph plots for MNIST dataset.  
Figure 5 highlights the differences in external metrics over time during model training on the MNIST dataset. Time is measured in minutes, and CPU utilization is based on the number of calls at that timeframe. We can see a more uniform distribution for PyTorch but overall lower peaks for LibTorch. Memory is measured in megabytes, whereas similarly, LibTorch uses less memory compared to PyTorch. This is due to the overhead from the Python environment. Both temperature curves peak at around 80 \({}^{\circ}\)C, beyond the normal operating limit for the Raspberry Pi 4. Power consumption, measured in milliwatts, is directly related to and estimated from CPU calls; therefore, we see a smaller overall footprint from Figure 5.
# Efficient Knowledge Distillation for Brain Tumor Segmentation
## 7 Conclusions  
In this article, the CD distillation method was proposed for brain tumor segmentation. A series of experiments were conducted on the BraTs2018 dataset. In future work, we will further explore the most appropriate combination of teacher and student networks. In addition, in some related studies, such as dataset distillation, we will apply the proposed knowledge distillation method to dataset distillation to address the problem of small datasets in medical images.  
Conceptualization, Y.Q., W.Z. and S.H.; methodology, Y.Q., W.Z. and X.W.; validation, Y.Q. and X.Y.; formal analysis, J.C. and S.H.; investigation, Y.Q. and X.Y.; writing--original draft preparation, Y.Q. and X.Y.; writing--review and editing, Y.Q., W.Z., J.C. and X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation of China (NSFC) 62006107 and by the Natural Science Foundation of Shandong Province (No. ZR2020 MF058, ZR2020 MF029).  
**Informed Consent Statement**: Not applicable.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Method** & \multicolumn{3}{c}{**Weight of Components**} & \multicolumn{3}{c}{**Dice Score (\%)**} & \multicolumn{3}{c}{**Hausdorff Distance**} \\ \hline  & \(\mathbf{\alpha}\) & \(\mathbf{\beta_{1}}\) & \(\mathbf{\beta_{2}}\) & **ET** & **WT** & **TC** & **ET** & **WT** & **TC** \\ \hline T: UNet & 0 & 0 & 0 & 78.83 & 85.69 & 85.81 & 2.77 & 2.57 & 1.66 \\ S: DeepResNetNet & 0 & 0 & 0 & 78.44 & 85.19 & 85.36 & 2.79 & 2.60 & 1.68 \\ \hline  & 0.9 & 0.1 & 0.9 & 79.54 & **85.95** & 88.07 & 2.72 & **2.52** & 1.53 \\  & 0.7 & 0.1 & 0.2 & 79.26 & 85.75 & 87.55 & 2.75 & 2.54 & 1.57 \\ S+ours & 0.9 & 0.1 & 50 & 79.50 & 85.90 & 88.03 & 2.73 & 2.54 & 1.55 \\  & 0.6 & 0.1 & 0.3 & 79.39 & 85.88 & 87.75 & **2.71** & 2.54 & 1.56 \\  & 1 & 0.1 & 0.9 & 79.14 & 85.68 & 87.73 & 2.74 & 2.53 & 1.56 \\  & 0.9 & 0.1 & 1.8 & **79.69** & 85.91 & **88.20** & **2.71** & 2.53 & **1.52** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The effects of component weights represented by hyperparameters \(\alpha\), \(\beta_{1}\) and \(\beta_{2}\) in Equation (6). The values in bold are the highest Dice score and the lowest hausdorff distance in their columns. As shown in the first two rows, when these weights were set to 0, as with the original segmentation network, the training process would be similar.
## 3 Results and Discussion
### Regimes of Hysteresis  
As the ions need to physically move to switch the device, the response time of the OECTs to a change of the input signal V\({}_{\text{GS}}\) is typically large. The response time also depends on parameters such as the ion mobility (conductivity of the electrolyte) or the distance between the channel and gate [1]. Therefore, the shape of the transfer curve (and the extracted parameters) depends closely on the V\({}_{\text{GS}}\) scan rate. We carried out a set of measurements with a fixed V\({}_{\text{GS}}\) step and different hold times to extract transfer curves with varying V\({}_{\text{GS}}\) scan rates (in units of mV s\({}^{-1}\)). The gate voltage was set at \(-1\) V for 15-30 s before each transfer curve measurement to ensure that the polymer reached the maximum conducting state. Holding V\({}_{\text{GS}}\) at the beginning of each measurement also acted as a pause, allowing the cations to move completely out of the polymer to mitigate any bias stress effect. Nevertheless, the duration of these measurements with different scan rates was around 20 min, which is much shorter than the time scale on which we observed the bias stress instability (see Appendix A.5 Figure A5).  
Figure 2b depicts the scan-rate-dependent transfer curve measurements for the OECT made from the 1 h-annealed PEDOT:Tos film. We utilized the \(\psi\) parameter to represent the hysteresis strength, which is the ratio of a pair of outputs I\({}_{\text{D}}\) to the same input V\({}_{\text{GS}}\), integrated over the hysteresis window [19] (see Figure 2b inlet). The \(\psi\) parameter indicated that the hysteresis saturated at a scan rate of \(\approx\)85 mV s\({}^{-1}\) for the 1 h-annealed devices. We assumed that the kinetic hysteresis observable under a faster scan rate was indeed due to the incomplete movement of the ions, as shown earlier [20]. However, the nature of the inner non-kinetic hysteresis at lower scan rates was not trivial.
## 2 Datasets
### NDBC Buoys  
The NDBC moored buoy networks in coastal waters provide real-time measurements of meteorological and oceanographic processes occurring directly on the sea surface [44],  
Figure 2: The along-track significant wave height (SWH) map from the Haiyang-2B (HY-2B) and Jason-3 altimeters taken on 15 May 2020 between (10\({}^{\circ}\)N, 100\({}^{\circ}\)E) and (60\({}^{\circ}\)N, 180\({}^{\circ}\)E).  
i.e., wind and waves. These recorded data are openly accessed for scientific research worldwide. Furthermore, its routine, continuous, and on-scene observations are critical for the error correction of weather and ocean forecasting, as well as for the validation of scatterometer, altimeter, SAR [45], and CFOSAT SWIM [46] data. In this work, the NDBC buoys in the East Pacific Ocean and the Gulf of Mexico were collected for the year 2020, and they were used to confirm the applicability of the fusion wind and hindcasting waves from the WW3 and SWAN models. We note that the wind data from the ASCATs and WindSAT were measured at a 10 m height above the sea surface, and thus, the following equation was employed to convert the sea surface wind speeds from the buoys into values at 10 m heights by using the logarithmically variable wind profiles [47]:  
\[U(z_{10})\ =U(z)\ln(\frac{z_{10}-z_{0}}{z-z_{0}}), \tag{1}\]  
where \(U(z)\) is the buoy-measured wind speed at height \(z\), \(U(z_{10})\) is the wind speed at a 10 m height, and \(z_{0}\) is the roughness length (assumed to be constant of \(1.52\times 10^{-4}\)).