esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of Damage Segmentation Based on Pixel-Level Data Balance Using VGG-Unet
## 6 Pixel-Level Data Balance  
### Process  
The previous research shows that Cropping Segmentation should have a better performance on detecting objects. To improve the capability of minor damage detection, especially for cracks, Background Data Drop Rate (BDDR) is defined to control the proportion of pixels in each category from the training and validation data set. BDDR means the percentage of randomly dropped out images in the background. The example of dropping background data is shown as Figure 11. After cropping original images, the background images are automatically separated from the data set by Python code, by counting whether there are labeled pixels from the corresponding annotations. If there are no pixels labeled as damage from annotation file, the cropped images are regarded as background images. Then these background images are randomly deleted from the data set, according to the given BDDR. After that, the left images are mixed with damage data to be a new data set for training VGG-Unet model. In this research, the testing data set isn't changed, due to it is not used in training the VGG-Unet model and does't effect the accuracy of the model.  
The original data set is the cropped images from rubber bearing, which is shown in Table 2 as above. Another four different BDDR parameters are tested: 0.2, 0.5, 0.8 and 0.9. Table 4 shows the detail of data set and the number in parentheses represents the quantity of images with damage. By enlarging the value of BDDR, the percentage of damage images is becoming larger and larger.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Value of BDDR** & **Training** & **Validation** & **Testing** \\ \hline
0 & 34,626 (1968) & 5027 (409) & \\
0.2 & 28,094 (1968) & 4103 (409) & \\
0.5 & 18,297 (1968) & 2718 (409) & 4218 (261) \\
0.8 & 8499 (1968) & 1332 (409) & \\
0.9 & 5233 (1968) & 870 (409) & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The proportion of data set for segmentation.  
Figure 11: Process of dropping background data.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Data Set** & **Method** & **PA** & **MPA** & **MIoU** & **FWIoU** & **RWIoU0.1** \\ \hline \multirow{2}{*}{Corrosion on steel} & Squashing & 75.6 & 57.7 & 44.2 & 62.7 & 46.8 \\  & Cropping & 81.6 & 70.4 & 57.1 & 71.4 & 59.1 \\ \hline \multirow{2}{*}{Damage on rubber bearing} & Squashing & 99.2 (98.7) & 75.8 (55.4) & 74.4 (52.7) & 98.7 (97.8) & 80.7 (64.3) \\  & Cropping & 99.4 (99.0) & 73.8 (51.4) & 73.4 (50.7) & 98.9 (98.0) & 79.9 (62.8) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of Damage Segmentation.  
The MDR of data set for Damage Segmentation is shown in Table 5. From the table, it can be found that with the increasing of BDDR, MDR also increases a lot, from 0.7% to 4.6% for training data set and from 1.6% to 9.1% for validation data set.
## 3 Results and Discussion
### Batch Studies
#### 3.2.3 Concentration Effect on the Adsorption of MB and IBF  
Figure 7 shows the adsorption capabilities of AC-MOP for MB and IBP in the solution at various initial concentrations. The trend from the plot indicates an increasing uptake with increased pollutant concentrations. This means that MB and IBP uptake on AC-MOP depended on the concentration. A similar observation was obtained in the [PERSON] et al. (2022) [30] study. This type of observation is explained by [PERSON] et al. (2019) [39], stating that the increase in concentration is due to the high mass transfer resistance of pollutants in the liquid and solid phases. The maximum sorption capacities for MB and IBF were 73.74 and 77.23 mg/g, respectively.  
The Langmuir and Freundlich models estimated the isotherms in Table 4. The Langmuir model suggests the process is achieved via monolayer adsorption of pollutants on the material, and the sorption is homogeneous [39]. This model also proposes that the material has binding sites, and every site is accountable for binding a single pollutant with  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Thermodynamic Parameters** & **MB** & **IBF** \\ \hline \(\Delta\)S\({}^{\circ}\) (k/mol/K) & 0.3923 & 0.1445 \\ \hline \(\Delta\)H\({}^{\circ}\) (k/mol) & 60.2224 & 70.1356 \\ \hline \(\Delta\)G\({}^{\circ}\) (kJ/mol)\(-\)25 \({}^{\circ}\)C & \(-\)2.3445 & \(-\)6.5656 \\ \hline \(35\)\({}^{\circ}\)C & \(-\)3.6532 & \(-\)4.3412 \\ \hline \(45\)\({}^{\circ}\)C & \(-\)5.6723 & \(-\)5.8844 \\ \hline \(55\)\({}^{\circ}\)C & \(-\)8.1223 & \(-\)2.7723 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thermodynamic results for the adsorption of MB and IBF.  
Figure 6: Temperature effect for AC-MOP under the initial concentration of 100 mg/L and 0.4 g dosage, pH 5.5 and agitation at 200 rpm for 2 h.  
no interaction between the adsorbed ions [40]. The data showed that regression coefficients obtained for MB and IBF on AC-MOP were 0.9987 and 0.9975, respectively, and the best fit was the Langmuir model.
# A Molecular View on Biomaterials and Dental Stem Cells Interactions: Literature Review
## 4 Dental Stem Cells and Biomaterial Interactions
### Mechusensors in Dental Stem Cells
For example, \(\alpha\)2\(\beta\)1 integrin recognizes the Asp-Gly-Glu-Ala amino acid sequence in collagen, while \(\alpha\)5\(\beta\)1 recognizes the RGD sequence in fibronectin and \(\alpha\)v\(\beta\)3 in vitronectin [101]. The adhesion process in animal cells is mediated by integrins, which generate supramolecular protein complexes with cytoskeletal proteins called focal contacts (FAs) (Figure 3). FAs are networks of proteins that provide structural integrity to cells and constitute a dynamic bridge between the ECM and actin of the cytoskeleton (Figure 3). In the case of stem cells, this process is essential for tissue integration of the biomaterial. In this context, [PERSON] et al. (2014) [102] studied the interaction between PDLSCs and a mussel-inspired polydopamine (PDA) adhesive biomaterial through the expression of integrins \(\alpha\)5 and \(\beta\)1. The authors demonstrated that integrin-PI3K linkages mediate cell adhesion and regulate osteogenic differentiation of PDLSCs [102]. Likewise, using silica-based materials, [PERSON] and colleagues [103] investigated the role of \(\alpha\)v integrin in the odontogenic differentiation of DPSCs. They reported that the silicon-containing biomaterial favored cell adhesion through increased fibronectin adsorption and integrin expression [103]. [PERSON] et al. (2013) [104] analyzed the effect of crystal alignment (ordered/disorganized) of apatite (an enamel-like substrate) on the expression of adhesion-related genes to produce an enamel/dentin superstructure in vitro. The authors reported that the ordered alignment of apatite provided a favorable environment for DPSCs adhesion, which was linked to the upregulated expression of integrins alpha 7 and 8 (ITGA 7 and 8), integrin beta 3 and 4 (ITGB3 and 4), vitronectin receptor-integrin alpha V (ITGAV), and the key adhesion protein fibronectin 1 (FN1) [104].
## 3 System Models  
### Federated Learning Preliminary  
The idea of FL is to enable distributed clients to cooperatively train a global model such that the global loss function, denoted as \(\mathbf{\mathcal{F}}(\mathbf{\omega})\), can be minimized. That is,  
\[\operatorname*{arg\,min}_{\mathbf{\omega}}\mathbf{\mathcal{F}}(\mathbf{\omega})= \operatorname*{arg\,min}_{\mathbf{\omega}}\sum_{i\in\mathbf{\mathcal{I}}}\frac{|\mathbf{ \mathcal{D}}_{i}|}{|\mathbf{\mathcal{D}}|}f_{i}(\mathbf{\omega}), \tag{1}\]  
where \(\mathbf{\omega}\) is the set of the parameters for the global model, \(\mathbf{\mathcal{I}}\) is the set of the selected clients, \(|\mathbf{\mathcal{D}}|\) is the number of the training data samples of all the clients, \(|\mathbf{\mathcal{D}}_{i}|\) is the number of the training data samples at client \(i\) (where \(\mathbf{\mathcal{D}}=\bigcup\limits_{i\in\mathbf{\mathcal{I}}}\mathbf{\mathcal{D}}_{i}\)), and \(f_{i}(\mathbf{\omega})\) is the local loss function of client \(i\), i.e.,  
\[f_{i}(\mathbf{\omega})=\frac{1}{|\mathbf{\mathcal{D}}_{i}|}\sum_{n\in\mathbf{\mathcal{D}} _{i}}f(\mathbf{\omega},\mathbf{a}_{i,n},b_{i,n}). \tag{2}\]  
Here, \((\mathbf{a}_{i,n},b_{i,n})\) is the input-output pair for the \(nth\) data sample in user \(i\)'s data set, and \(f(\mathbf{\omega},\mathbf{a}_{i,n},b_{i,n})\) captures the error of the local model (with parameter \(\mathbf{\omega}\)) over \((\mathbf{a}_{i,n},b_{i,n})\). In each global iteration, FL comprises four steps.  
1. Server broadcast: In the \(k\)-th global iteration, the FL server broadcasts the global model generated in the previous global iteration, denoted as \(\mathbf{\omega}^{(k-1)}\), to all the selected clients.  
2. Client local training: Each client \(i\) trains its local model over its local data set \(\mathbf{\mathcal{D}}_{i}\), i.e., \(\mathbf{\omega}_{i}^{(k)}=\mathbf{\omega}_{i}^{(k-1)}-\delta\
abla f_{i}\Big{(}\mathbf{ \omega}_{i}^{(k-1)}\Big{)}\), where \(\delta\) is the learning rate.
3. Client model uploading: After deriving the local model \(\mathbf{\omega}_{i}^{(k)}\), client \(i\) uploads its local model to the FL server.
4. Server model aggregation: The FL server aggregates the local models from the clients and updates the global model based on, for example, FedAvg [5], i.e., \(\mathbf{\omega}^{(k)}=\sum\limits_{i\in\mathcal{I}}\frac{|\mathbf{\mathcal{D}}_{i}|}{| \mathbf{\mathcal{D}}|}\mathbf{\omega}_{i}^{(k)}\).  
The global iteration keeps executed to update the global model \(\mathbf{\omega}^{(k)}\) until the global model converges.
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 5 Material and Methods
### UE Transmission/Reception Range and CQI Calculation  
Even if the direct LTE max transmission range can reach 500 m [18], realistic values are generally located between 100 and 200 m [19] depending on the area topology and taking into consideration all sources that can affect signal strength/noise. The determined CQI values are listed in Table 2, which shows a corresponding modulation/coding scheme with the minimum transmission rate expressed in kbps.  
The uplink CQI is determined proportionally to the distance \(d\) between two UEs by considering the maximum transmission range \(R\) of each user, as illustrated in Algorithm 1.  
Figure 14: UE movement in the Random Direction Mobility model.  
Figure 13: Adding UEs to the simulation area over time.
## 3 Results and Discussion
### Influence of Desorption Temperature on Dehumidification Index  
Figure 4 shows the variation of the dehumidification index under different desorption temperatures. It suggests that in enhancing the desorption temperature, the average dehumidification index has a tendency to increase, mainly due to the effect of the humidity ratio [30]. The important key of the dehumidification process is the rate of the water vapor adsorbed, which is a function of the mass transfer. This mass transfer is driven by the partial pressure of water vapor and its saturation pressure. High vapor pressure at the elevated temperature guiding to more mass transfer occurs. Accordingly, the high moisture content in the air provides a high driving force for moisture transport; hence, it increases the dehumidification index. When increasing desorption temperatures from 308, 318, 238, 338, and 345 K, the average dehumidification indexes increased from 1, 3, 5, 6, and 6.8 g/kg, respectively.  
Figure 3: Water content removal during adsorption/desorption processes for different desorption temperatures.
## 5 Performance Results of RACD Strategy
### Performance Results
#### 5.2.2 Impact of the Cache Size on the Performance of Our Strategy  
Figures 5 and 6 show the effect of cache size on the performance of the data sharing and data resource allocation strategy in terms of hit ratio and hop reduction ratio. From the two figures, we can see that, for the sharing strategy, the hop reduction and hit ratio values increase with the cache size, with a large margin for the OD-Sharing results compared to the UD-Sharing. The improvement in results when cache size increases makes sense because now more data are cached, which increases the chances of having the data searched to the closest possible node. The difference in the results between the two classes is due to the fact that the requests in the OD-Sharing class are more numerous than those of UD-Sharing.  
Figure 4: Caching performance vs. priority level in terms of (**a**) cache hit ratio, and (**b**) hop reduction ratio.  
Figure 5: Caching performance vs. cache size in terms of: cache hit ratio.  
Figure 6: Caching performance vs. cache size in terms of hop reduction ratio when varying \(k\).  
For the resource allocation strategy, with \(k=0.1\), the university data (UD-RA) achieves a higher hit ratio and hop reduction ratio as the cache size increases. The OD-RA achieves low and constant values compared to UD-RA. This is because, when using a very small value for coefficient \(k\), the cache partition size given to UD-RA is much larger than the size given to OD-RA, which was small and fixed in all sizes tested. When \(k=0.9\), we find that our strategy performs inversely to \(k=0.1\) where, as the cache size increases, OD-RA performs well compared to UD-RA in terms of hit ratio and hop reduction ratio. The reason is that, when the coefficient is \(k=0.9\), the coefficients of the two data classes become almost equivalent; therefore, the allocations of each partition become related to the amount of data received from both classes. Since the consumed university data represents only 4% of the total consumption, all the cache is allocated to OD-RA data. We also notice that, when \(k=0.4\), from 10 to 30, the UD-RA obtains a null hit and hop reduction ratio, in contrast to the OD-RA, which increases with the increase in cache size. From 40 to 60 we notice an increase in the results of both classes of data, with their convergence until they are equal in the capacity 70. From 80 to 100, the results show a continuous increase in both classes, with dominance of UD-RA over OD-RA. The reason for the continuous increase in results for both classes is that both classes obtain more space when the total cache space increases. The reason why these results are obtained when \(k=0.4\) is that, when the cache size is between 10 and 30, the Lagrange utility function gives the total memory space for the OD-RA. Then, beyond size 40, the function starts to give more memory space for UD-RA, and OD-RA space remains fixed until they reach equal results for size 70. Then, the increase in space continues for UD-RA, which takes more memory space, and thus achieves better results than OD-RA.
# Elevator Fault Diagnosis Method Based on IAO-XGBoost under Unbalanced Samples
## 3 Sample-Balancing Method Based on RFE-SMOTE-Tomek  
In reality, the fault data samples of the elevator are much smaller than the normal data samples, and there is an imbalance of sample categories in the obtained dataset. This imbalance hinders effective model training and fault diagnosis. To address this problem, RFE-SMOTE-Tomek is proposed to oversample the samples. Redundant features can result in the generation of new samples that are not conducive to model training. RFE combined with random forests performs feature filtering on raw features to remove redundant features while reducing the dimensionality of the data. After feature optimization, the new samples generated are more meaningful and the quality of oversample is improved. The optimized features are oversampled by SMOTE-Tomek to obtain the sample balanced dataset. The flow is shown in Figure 3.
## 3 Results and Discussions
### Assumptions and Limitations  
Several assumptions were made in the numerical model that may limit its applicability to different configurations and materials. First, the boundary conditions posed challenges in the finite element model (FEM), particularly due to the cylindrical steel pipes used as supports in both static and dynamic tests. These supports restricted free rotation and induced tensile forces along the specimen's longitudinal axis, which could not be fully replicated in the FEM. Additionally, the painted surfaces and pipe compression further contributed to deviations between the experimental and numerical results.  
Second, the FEM excluded spring dynamics, which may lead to minor discrepancies concerning energy dissipation. While this simplification was made to focus on impact energy, excluding spring potential energy may result in slight differences between the physical setup and the numerical model.  
Lastly, noise in the impact force data collected during experiments introduced fluctuations that further contributed to misalignment between the physical setup and the FEM results.
## System Specification  
### System Composition  
The AS/RS-based picking system structure discussed in this paper includes three parts: storage unit, transport unit, and picking unit. The detailed system composition is shown in Figure 1.  
The storage unit consists of high-rise shelves and stackers. In the AS/RS discussed in this paper, the high-rise shelves provide sufficient storage compartments, and each compartment can store a standard container. Each container, as a SKU, usually stores multi-shipbuilding material items with separate material identification codes, and the orders to pick in the task are included in these materials. The stackers of storage units referred to in this paper are the most common medium-sized double-column stackers. One stacker is arranged in one aisle and each stacker is responsible for the storage and retrieval of containers. The handover points between the stackers and AGVs are set at the exit of the aisles.  
The transport unit consists of multiple AGVs of the same model. When the AGVs are idle, they can be on standby or charged in the designated charging area.  
The picking unit includes multiple picking stations with workers for parallel operations, each of which is managed by a picking worker. Considering the space limitations of the picking station, as well as the difficulty and probability of errors for workers, this paper sets an upper limit of three pallets that can be picked simultaneously at each picking station.
## 6 Discussion
### Heterogeneity of Spectral Signatures at the Moderate Spatial Resolution  
Satellite-based predictors (i.e., NDVI, LST, VCI, TCI) used in the proposed crop yield forecasting system are not crop specific due to coarse spatial resolution of pixels (\(\geq\)300 m) which may cover several agricultural fields sown with different plants. Nevertheless, such heterogeneous spectral signatures have been successfully used as a proxy for the vegetation condition and consequently as a predictor of crop yields (e.g., [11]). Yet, the new generations of satellite sensors such as Multispectral Instrument (MSI) onboard Sentinel-2 satellite constellation acquire images at both high temporal (\(\sim\)5 days) and spatial (\(\sim\)10 m) resolutions. This is a prerequisite to extract spectral signatures for individual fields and consequently to obtain a clear spectral signature related to physical characteristics of a particular crop [44]. Although the Sentinel-2 imagery is available, there are still some limitations that void its utilization within the crop yield forecasting systems. One of the limitations is the short Sentinel-2 data record (since 2015) which cannot be easily extended by means of cross-calibration with an older sensor (such as MODIS in this study) due to the unavailability of such an instrument featuring comparable spatial and temporal resolutions. A short time series hinders derivation of long-term anomalies and reduces the quality of crop yield forecast performed by a machine learning algorithm (Figure 15). Another limitation to the assimilation of high resolution satellite imagery to crop yield forecasts at a large/continental scale is the lack of extensive multitemporal reference information on crop yields and crop types at an agricultural field level. Nevertheless, these gaps are being quickly bridged by the expanding Sentinel-2 archive and by reference in-situ data on crop productivity collected by sensors mounted on novel agricultural equipment (e.g., harvesters). Consequently, several studies have already tackled the problem of crop yield forecasting based on high resolution Sentinel-2 imagery [45; 46]. The future extensions of the proposed system will allow for crop yield predictions at a field level. However, to date it is not possible due to data availability and quality issues. Thus, the presented system is based on medium resolution satellite imagery which does not allow monitoring of individual fields. However, it should be emphasized that the forecasting system has been designed in such a way that changing the input data from Sentinel-3 to Sentinel-2 and switching to the clear spectral signature of specific crops requires only adaptation of the data input module. Then, it would be also possible to assimilate the Copernicus high resolution vegetation phenology and productivity product [47], which provides information on crop development and productivity at the 10-m scale.
# Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images
## 3 Multiple Preprocessing Hybrid Level Set Model  
Because of the problems of OD segmentation being affected by PPA, bright noise, and blood vessels, this paper proposes a multiple preprocessing HLSM. A morphological method is able to avoid OD segmentation from being affected by blood vessels; the hybrid level set method with shape constraints is used to control the evolution of the contour and avoid the effect of bright noise; adaptive threshold-based ellipse fitting can find a more appropriate initial value of level set function and the effect of PPA can be simultaneously avoided. Figure 4 shows the flowchart of the proposed approach. In this section, \"contour\" means the contour of zero-level set, and \"boundary\" means the boundary of OD.