esa-sceva/satcom-chunk-collection huggingface dataset:
# Microstructural, Electrochemical and In Vitro Analysis of Mg-0.5 Ca-xGd Biodegradable Alloys
## 2 Experimental Details
### Cytocompatibility Analysis
#### 2.3.2 Cell Culture  
For the cell viability study, we used albino rabbit dermal primary fibroblasts (passage No. 3) cultured in a complete Dulbecco's modified Eagle medium/Nutrient F-12 Ham (DMEM-F12 Ham) culture medium. For this, 10% inactivated fetal bovine serum (FBS) and 1% antibiotic solution (containing 5000 units penicillin, 5 mg streptomycin and 10 mg neomycin/mL) were added to the cell culture media prior to use, and the cells were kept under standard cell culture conditions (i.e., a humid incubator at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\)). Briefly, after attaining the 90% confluence level, the cells were washed with phosphate-buffered saline (PBS) solution, detached by incubation with trypsin/EDTA (Sigma Chemical Co., St. Louis, MA, USA), used for seeding in 24-well culture plates at a density of 1 \(\times\) 10\({}^{4}\) cells/well and then incubated for 24 h to facilitate cell adhesion on the wells' bottom. Subsequently, the medium was changed with complete fresh medium, after eliminating the unattached or dead cells by washing with PBS [44; 45]. Afterwards, the experimental Mg alloy samples placed in well-permeable supports were suspended on the top of the 24-well-plated cell cultures and co-incubated under the abovementioned conditions for 1, 3 and 5 days. The medium was refreshed every 2 days. A similar methodology was applied for both the cytocompatibility assay and the cell morphology evaluation [46]. It should be pointed out that our method favored the interaction/reaction between the studied Mg alloys with the cell culture medium [47]. In this study, we used control wells (i.e., negative controls) containing cell cultures only (i.e., 3 control wells for each time period).
## 2 Related Works
In this situation, the responsiveness of the edge servers to guarantee seamless video streaming performance might be very poor in that the original purpose of the MEC is to ensure swift data processing of IoT data to provide low latency responses to devices. Video is massive data requiring reliable and high bandwidth connection for its delivery from servers to users. To reap the full benefits of MEC, content caching policies must efficiently cache and offload lightweight tasks to the edge servers. A more suitable solution involves clustering and cooperation among MEC servers, with at least one resource-sufficient node able to take heavy tasks on behalf of the other nodes [36; 37]. Edge caching policies with effective edge resource allocation are key to maintaining a fine balance in computation and storage in the IoT Edge system. The following works [38; 39; 40; 41; 42; 43] establish the edge caching problem as a resource-constrained optimization problem, decomposed into subproblems, and solved mostly with heuristic algorithms. The solutions are not always optimal, but evaluations show improvements over state-of-the-art caching policies considered in the works. Worldwide attention has been drawn to the global carbon emission contribution of the information-communication technology (ICT) industry. Today, the Internet is the most needed and desired entity, connecting every facet of life and every sector of major economies, whose absence or malfunction causes heavy losses of monetary capital. The core and backhaul networks are often congested with ceaseless data traffic. Edge computing promises to reduce the over-dependence on the core and backhaul networks. Several studies have proposed edge caching policies that also capture techniques of further reducing the backhaul traffic [44; 45; 46]. However, the proposed schemes have not analyzed unused buffered content data during streaming session abandonments. The existing approaches tackle the edge caching problem centrally, with content transcoding tasks offloaded to designated computing units without real-time balance in available computing resources in a dynamic and unpredictable wireless streaming environment. Additionally, no consideration has been given to the wastage of computingand communication resources on unused content parts whenever streaming sessions are abandoned. Our proposed scheme addresses the problems of inefficient cache storage management and backhaul resource wastage, due to streaming session abandonments, exploiting a new method of content caching with strict file view duration threshold assignment, systematically designed to achieve higher cache hit ratios through the dynamic placement of more contents at the edge, and fewer content transmissions via the backhaul network.
# A Machine Learning Approach for Breast Cancer Risk Prediction in Digital Mammography +
## 3 Results
### Machine Learning
#### 3.2.1 Oversampling Techniques  
One of the proposed solutions to solve the imbalance problem is to apply oversampling techniques such as ROSE or SMOTE on the datasets. For the feature selection, the redundant features were initially removed with the use of the correlation matrix. As with the other selection techniques, some features fail to discriminate on an unbalanced dataset since they are based on the use of a classifier. Therefore, the features selected are the 35 features obtained by applying the filter features selection methods. _SMOTE_ was applied on a dataset consisting of the largest class, that is, B, and the least numerous class, that is, A, generating an oversampling of class A and an undersampling of class B in order to obtain a number of elements per class, comparable with classes C and D. The results of the classification by applying _SMOTE_ can be observed in Table 2, for the overall accuracy, and in Table 3, for the accuracy relating to the individual classes. As can be seen from these results, the portion of TPP and TNP for the class that was less numerous in the starting dataset (class A) has higher values, while class B exhibits a lower value, which means that it improves the accuracy of class A, at the expense of class B; moreover, classes C and D do not seem to show improvements related to the fact that the dataset is balanced. As a result, the overall accuracy is not improved.  
Once _ROSE_ is applied, all classes are oversampled to match the number of elements of the largest class, which is B. The results of the classification by applying _ROSE_ can be observed in Table 2 for the overall accuracy, and in Table 3 for the accuracy relating to the individual classes. In this case, it can be seen that although class A was increased in the number of samples, as well as classes C and D, this does not allow the algorithms to acquire new information; therefore, the results in accuracy remain low both on the single class and overall. In conclusion, by applying the ROSE and SMOTE techniques, it can be seen that ROSE does not solve the problem of low accuracy on less numerous classes, while _SMOTE_ increases the accuracy of class A without significantly raising the overall accuracy, whose highest value obtained is equal to 63.33%, with the linear discriminant analysis model.  
Finally, the binarization described in [23; 24] turns out to be the best solution to solve the unbalancing problem, and this allows reaching a maximum of 93.55% accuracy, obtained with a Support Vector Machine model, substantially improving accuracy compared to multiclass classification on the original dataset.  
Figure 10: Box plots of Cohen’s Kappa indices for CC and MLO images.
## 3 Design of Hybrid Control Scheme  
From the analysis above, strong disturbance and large parameter perturbation exist in the controlled plant. Despite that, other uncertainties such as friction and the gap in mechanical connection, which are unknown disturbances, also exist. Therefore, designing a robust control method for the controlled plant is needed.  
Since robust control relates the amount of feedback at each frequency to the amount of uncertainty in the plant dynamics, the equivalent linear method forces the magnitude of the loop transmission to be unnecessarily large in the low-frequency range. If we take the robust approach directly, the result is an over-designed and conservative control system. Because it uses much more feedback gain than is actually required to solve the robust control problem, it does not even satisfy the performance requirements of the system. Thus, in order to effectively solve the control and synthesis for a loading system with large perturbation and strong motion disturbance, a hybrid control scheme is presented.
## 3 Experimental Study
### Influence of the Number of Eigenvectors in PCA  
Since NLM in PCA is used, it is intuitive to study the behavior of NLM with respect to different number of subspace dimensionality, which is shown in Figure 6. Note that if the number of dimensionality is equal to the number of pixels in local similarity window, NLM + PCA will degenerate to NLM. Figure 6 tells us that if the dimensionality is less adopted, the segmentation accuracy of MKCIS will decline slowly. The reason may be that the increased accuracy at lower values is that kernel distances calculated in the lower dimensional space are likely to be more accurate and robust than distance computed from the full-dimensional space because PCA discards the most irrelevant dimensions. In our study, about two or three eigenvectors are selected to implement the subspace projections. Clearly, about 98% of segmentation accuracy is obtained.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Index** & **KWFLICM** & **FLICM** & **FGFCM** & **KGC** & **MKCIS** \\ \hline E & 2.57(0.01) & 2.78 (0.01) & 2.57(0) & 2.55(0.03) & 2.55(0.01) \\ \hline \end{tabular}
\end{table}
Table 3: Statistical segmentation results of entropy (E) of the real SAR image over 30 independent runs.  
Figure 5: Mean values of partitioning accuracy of five algorithms in segmenting the synthetic SAR image by adding simulated speckle from 1-look to 10-look.
## Materials
### Data used for Geoscience Laser Altimeter System (GLAS) Leaf Area Index (LAI) Inversion  
#### 2.2.1 Ice, Cloud and land Elevation Satellite (ICESat) GLAS Data  
GLAS data products GLA01, GLA05, and GLA14 were used in our method; the used data were acquired from the laser campaigns L2c (2004/05/18-2004/06/21), L3c (2005/05/20-2005/06/23), and L3f (2006/05/24-2006/06/26). GLA01 is a Level-1A product and provides waveform information [i.e., received echo waveform (r_rng_wf) and emitted transmitted waveform (r_tx_wf)] and time information [i.e., starting address of the transmit pulse sample (i_TxWfStart) and ending address of the range response (i_RespEndTime)]. GLA05 is a Level-1B product and provides the gain values of the received waveform and transmitted waveform (i_gval_rcv and i_gval_tx). GLA14 is a Level-2 product that contains information about the properties of the GLAS measurements, including the footprint centroid coordinate (i_lat and i_lon), max amplitude of the received echoes (i_maxRecAmp), standard deviation of the background noise (i_sDevNsObj), reflectivity (d_reflctUC), and reflectivity correction factor for atmospheric effects (d_reflCor_atm).
## 3 Modeling and Data Sources
### Model Evaluation and Validation  
#### 3.2.1 \(\mathrm{R^{2}}\) Test of the Model  
In model validation, the coefficient of determination, \(\mathrm{R^{2}}\), serves as a pivotal metric to quantify the explanatory power of the model's independent variables over the variation in the dependent variable. Ranging between 0 and 1, values of \(\mathrm{R^{2}}\) approaching 1 generally indicate a higher degree of model fit, reflecting superior fit quality. Moreover, as an evaluative standard, \(\mathrm{R^{2}}\) aids in comparing the accuracy of different models, thereby providing a scientific basis for model selection [35]. A high \(\mathrm{R^{2}}\) value signifies that the model has effectively captured the intrinsic variability of the data, suggesting potential model efficacy. However, \(\mathrm{R^{2}}\) alone does not determine predictive ability, and high values might suggest overfitting. Hence, it is typically used in conjunction with other metrics (such as adjusted \(\mathrm{R^{2}}\), AIC, BIC, etc.) to comprehensively evaluate model performance.
# Effect of Particle Size on Composet Analysis by Portable X-ray Fluorescence
## 3 Results and Discussion
### Effect of the Sample Grinding on the Elemental Concentration Determined by pXRF
An alternative explanation for the differences in the element concentrations among the ground and unground samples can be based on the different organic matter contents that the different granulometric fractions could eventually have. In a study with soil samples, [PERSON] et al. [29] found a correlation between the results measured with pXRF and the organic matter content of the soil, among other factors. In [PERSON] et al. [29], the inclusion of the organic matter content allowed for significant improvements in the regression models for the concentrations of Pb and Sr. The organic matter contents of the soils in this study were between 3.9 and 33.4 g kg\({}^{-1}\), values much lower than those of the compost samples in our study (310-726 g kg\({}^{-1}\), Table 1). Additionally, under this hypothesis, it would be expected that the alperujo compost, with a higher content of organic matter (Table 1), would have presented more differences in the concentrations between the ground and unground samples (Tables 4 and 5), which was not observed. In a previous study by our team [20], with varied compost samples with different organic matter content, the Bal parameter, which is related to the organic matter content of the samples, was only relevant in the predictive linear fits of the S and Ni contents, elements that in the present compots did not show variations due to grinding. The differences depended on the type of compost, being very variable between them. In these circumstances, it was not possible to establish correction factors that allow for correcting the pXRF readings depending on whether the sample was ground. Several of the elements that presented significant variations are relevant from the point of view of the environmental (Pb, Zn, and Cu) and agronomic compost quality (the plant nutrients K and P, among others), and their contents in compost are regulated (for example, in Europe by the regulation [19]). Thus, users must be aware that pXRF readings may present deviations depending on the granulometry of the sample and its nature, and it would be advisable to adopt standardized criteria regarding the grinding and homogenization of the products. Although for a screening analysis, the pretreatment of the sample may be of less relevance, as other authors have indicated [11], if quantitative results are required, pretreatment (i.e., drying, grinding, and homogenization) is necessary. * Method of preparation affects pXRF data quality;
* Accuracy of pXRF data may be element specific [30].
# Evaluation of Spaceborne GNSS-R Retrieved Ocean Surface Wind Speed with Multiple Datasets
## 3 Results and Validation
### Wind Speed Retrieval Based on CCMP
The primary contribution is from the insufficient testing samples at winds above 15 m/s, which also occurred on the training samples, where it appears as a large modeling error, and the observation is very sensitive to the variant of wind speed. Figure 11: Root Mean Square and bias from CCMP-derived sea surface wind speed in different wind bins. Figure 10: Log(density) scatterplots of retrieved wind speed with the CCMP ground truth wind speed (**a**), residual versus truth wind speed (**b**), incident angle (**c**), and RCG (**d**).
## 6 Conclusions  
We underscore the transformative potential of the Local Model Evaluation Client Selection (LMECS) algorithm within FL for patient severity analysis in multi-institutional emergency room settings. This work primarily focused on addressing the challenges posed by existing FL and centralized data analysis methods, particularly in terms of data privacy, computational efficiency, and the diverse nature of medical data. The LMECS algorithm, an innovative contribution to FL, has demonstrated its ability to adapt to various user-specific tasks, offering not just compatibility with the stringency of medical data privacy but also a substantial improvement in performance metrics compared to existing FL algorithms. Significantly, the experimental results revealed that LMECS could match, and in some instances surpass, the performance levels of centralized learning (CL). This is a critical breakthrough, suggesting that it is possible to achieve the efficiency and accuracy of centralized models while retaining the inherent benefits of FL, such as data privacy and reduced data movement. Moreover, our findings highlighted a significant reduction in execution time with the implementation of the LMECS algorithm. This reduction was observed when compared both to CL and existing FL algorithms. Such efficiency is especially crucial in clinical settings, where prompt decision-making based on accurate and timely data analysis can have a substantial impact on patient outcomes. This aspect of LMECS underlines its suitability for real-world healthcare applications, where both speed and accuracy are paramount. Another crucial aspect of our research findings is the enhanced performance of the proposed LMECS algorithm compared to standalone learning (SL), where FL is not applied. The experimental results indicate that LMECS significantly improved the overall performance of local models. This improvement was not limited to specific metrics but was observed across a range of performance indicators, demonstrating the algorithm's ability to enhance model accuracy and reliability in diverse medical data environments. In terms of metric-based performance evaluation, LMECS's adaptability was evident. The ability to tailor the algorithm based on specific performance metrics like loss, accuracy, and AUC enables a more nuanced approach to model evaluation. In the future, applying LMECS to multiple classification and prediction models involving data from a variety of hospitals will add significant weight to the work. This real-world applicability is important to state the effectiveness of the algorithm beyond theoretical or simulated environments. In summary, this work not only contributes to the field of medical data analysis and patient care but also opens new avenues for further research. The advancements in FL brought about by LMECS can be explored in other domains where privacy concerns and data heterogeneity are predominant. The future of FL, particularly in healthcare, looks promising, with LMECS paving the way for more efficient, private, and accurate data analysis methods.
## 3 Simulation Environment and Approaches
### Software and Hardware Configuration for PIL and HIL
#### 3.3.3 Helmholtz Cage Configuration for HIL  
For HIL simulation, an approximation of Earth's magnetic field is required. This was achieved using a high-precision magnetic testbed, the IS501 NMTB, which boasts an accuracy of 99.89% in reproducing the desired magnetic field along the satellite's orbit [13]. Additionally, the HSF-100 magnetometer was selected for measuring the magnetic field across the three axes, with the primary objective of calibrating and providing data every second. The magnetometer data were transmitted to the computer controller via USB and have their own data acquisition system.  
Furthermore, the TCP/IP protocol was employed for communications between the high-precision DC Sources and a router, which was connected to the computer via Ethernet. This setup necessitated prior knowledge of its IP address to send appropriate commands to power the Helmholtz coils. In addition, Simulink received an input of 27 strings, which contained the magnetic field measurements of HSF-100, which were converted into doubles, while the computer controller sent an output packet sized at 200 bytes. This packet included the voltage value and commands directed to the high-precision DC Sources. An example of an initial command sent includes functions such as start mode, command control mode, maximum current, delay, and initial voltage.  
Furthermore, for calibration purposes, both the magnetic field and voltage were sent to the workspace to fit a linear model. This process enabled the determination of the necessary voltage required to achieve a specific magnetic field. Subsequently, in magnetic field control, a PID controller was employed to mitigate the offset inherent in the linear model. The input to the controller was the error signal, derived from the disparity between the actual magnetic field value measured by the magnetometer and the desired setpoint acquired from the WMM (World Magnetic Model) expressed in the CubeSat's body frame \(\mathcal{B}\). The physical configuration and schematic representation for the proposed testbed are shown in Figure 9 and Figure 10, respectively.  
Finally, in order to enhance accuracy of magnetometer measurements, before conducting the HIL test, a magnetometer calibration was implemented via a fitting ellipsoid algorithm. Furthermore, taking advantage of the proposed testbed, an on-orbit calibration was conducted during the phase of detumbling control. Afterward, this calibrated data was utilized for the HIL test.  
Figure 8: Real-time emulator for Processor in the Loop. This configuration also serves as a basis for Hardware in the Loop.  
Figure 9: Hardware-in-the-Loop environment incorporating IS501 NMTB module.
# Model-Driven Approach to Cloud-Portability Issue
## 4 Entities of Existing IaaS Platforms
### Amazon Web Services
#### 4.1.2 Virtual Machine  
Virtual machine (VM) is one of the base entities within topologies. In scripts, it is referred to as AWS::EC2::Instance. VM has one mandatory parameter, Imageld, which is a reference to the image of the operating system from which the VM will be cloned. All other parameters are optional. The InstanceType parameter represents a template according to which VM will be assigned computing resources, number of CPU cores, amount of RAM memory or hard disk size. Another parameter is security groups, which works as a packet filter. The AvailabilityZone parameter denotes the Amazon data center where the VM is created. Nowadays, Amazon has data centers in 21 geographical locations all over the world. The parameter SubnetId is the subnet to which the VM will be connected. The KeyName parameter is the SSH key that is used for remote access to the VM. One of the very important parameters is UserData. Using this parameter, it is possible to run a shell script during the first boot of a VM. A custom shell script can be written in any language that is supported by the guest operating system. Based on these scripts, it is possible to modify the VM, as is required. The last parameter is Tags, which represents just simple attributes that help administrators to easily identify virtual instances.
# Surface Defect Detection Model for Aero-Engine Components Based on Improved YOLOv5
## 3 Experiment and Results
### Comparison of Different Object Detection Algorithms  
In order to comprehensively evaluate the performance of the YOLOv5s-KEB model, we selected the Faster R-CNN, YOLOv3, YOLOv4 and YOLOv4-tiny object detection algorithms for experiments. We used the same dataset to train and validate each algorithm and then determined the detection performance for different algorithms. The results are shown in Table 5.  
As shown in Table 5, since Faster R-CNN is a two-stage object detection algorithm with poor real-time performance, its detection speed was the lowest. The detection speed of YOLOv4-tiny was the highest, up to 85.08 FPS, but the detection effect for small defects was poor, and the mAP was only 50.1%, which was far lower than other algorithms. The detection accuracies of YOLOv3 and YOLOv4 were high, but the detection speeds were low and the model sizes were quite large. Compared with other object detection algorithms, the YOLOv5s-KEB model had the highest defect detection accuracy and the smallest size. The mAP was 98.3% and the weight was 16.5 MB, both of which were far superior to other algorithms. Furthermore, it has a lightweight structure, which saves training time and makes it easy to deploy in mobile terminals.