esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Study  
### Performance Evaluation  
For the evaluation procedure of our convolutional autoencoder, we used several metrics, which are the following: global accuracy, AUC_ROC, which is the Area-Under-Curve (AUC) of Receiver-Operating-Characteristic (ROC), specificity, f1-score, sensitivity  
Figure 5: The architecture of the proposed autoencoder. Each column of hidden layers represents a bigger layer.  
and precision. We also represented the ROC curve for each dataset. These evaluation metrics were calculated based on the True Positive (TP), True Negative (TN), False Positive (FP) and False Negative (FN) rates. Global accuracy is a metric used to measure the ratio of correctly classified pixels to the total number of pixels in the dataset. Specificity measures the proportion of the negatives correctly identified, while sensitivity, also known as recall, measures the proportion of the positives correctly identified. Finally, F1-score concerns the recall's harmonic mean (average) and precision [29]. Those metrics have the following mathematical definitions:  
\[\text{ACC}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+\text{ FN}}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}}\]  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}}\]  
\[\text{F1 Score}=\frac{2\times(\text{Recall}\times\text{Precision})}{\text{ Recall}+\text{Precision}}\]  
The final results of our proposed model are promising. We tested our autoencoder on both the DRIVE and the STARE databases. First, the DRIVE dataset's metrics are AUC_ROC = 97.49, accuracy = 95.73, specificity = 98.57, f1-score = 81.0, precision = 88.0 and sensitivity = 75.0. On the STARE dataset, the metrics are AUC_ROC = 97.57, accuracy = 96.92, specificity = 98.7, f1-score = 79.0, precision = 82.0 and sensitivity = 75.45. Finally, the ROC curves of our introduced model on each of the databases mentioned above are presented in Figure 6. Also, an example segmentation case of our methodology is illustrated in Figure 7.
# Research on the Compactness of Lunar Soil Simulant Based on Static Cone Penetration Test
## 3 Results and Analysis
### Model of the Relationship Between Cone Tip Resistance and Compactness
When the compactness exceeds 70%, the cone tip resistance begins to increase significantly, and the curve shows a clear upward trend. When the compactness approaches 90%, the growth rate of the cone tip resistance accelerates. There is a positive correlation between compactness and cone tip resistance: the higher the compactness, the greater the cone tip resistance, as shown by the high-compactness lunar soil simulant. When the overall compactness is at a low level, the cone tip resistance increases relatively slowly with the increase in compactness and in a relatively low range. When the overall compactness is at a medium level, the growth rate of the cone tip resistance starts to accelerate, and the value increases significantly. This indicates that within this compactness range, the contact between lunar soil particles gradually becomes closer, and the friction and interlocking forces between particles begin to have a greater impact on the cone tip resistance. When the overall compactness is at a high level, the cone tip resistance shows a rapid growth trend with the increase in compactness. This indicates that the interaction between lunar soil particles is extremely strong and the structure of the lunar soil becomes very dense under high-compactness conditions. When the compactness increases from 68% to 78%, the cone tip resistance increases from 3.262 MPa to 11.044 MPa. When the compactness increases from 78% to 86%, the cone tip resistance increases from 11.044 MPa to 38.349 MPa. This shows that within the relatively high compactness range, a slight increase in compactness can lead to a substantial increase in the cone tip resistance.
## 2 Materials and Methods
### HyPlant Processing Chain
#### 2.2.4 HyPlant FLUO: SIF Retrieval--Processing Cluster IV  
The fourth cluster of the HyPlant processing chain deals with the SIF retrieval, based on the non-deconvolved/deconvolved at-sensor radiance of the FLUO data produced in the previous cluster, and is illustrated in Figure 7. The different SIF-retrieval algorithms are applied to non-georectified and non-georeferenced image cubes. The GLT file is only applied at the end (after SIF retrieval has been conducted) to georectify the SIF maps.  
The terrestrial oxygen absorption features, located at 760 (O\({}_{2}\)-A) and 687 nm (O\({}_{2}\)-B), were mostly used to quantify the SIF signal. Over the years, different approaches have been developed, namely 3 FLD [36], cFLD [37], eFLD [38] and iFLD [39], which are all based on the Fraunhofer line discriminator (FLD) principle [40; 41] and determine SIF using one spectral band inside and one outside the absorption feature. Whereas FLD-based methods only allow the determination of SIF at a specific wavelength, spectral fitting methods provide quantitative SIF information for all spectral bands within the oxygen absorption features by using a curve-fitting algorithm (e.g., [42]). Alongside methods using the atmospheric oxygen-absorption bands, different approaches based on solar absorption features have been developed, which are independent of atmospheric modeling and can be distinguished in simplified  
Figure 6: Spectral characteristics of the HyPlant FLUO module in the O\({}_{2}\)-A (right) and the O\({}_{2}\)-B absorption feature (left). (**a**) figures show the at-sensor-radiance of green vegetation with the characteristic absorption features located at 687 and 760 nm. (**b**) and (**c**) illustrate the fine spectral resolution (FWHM) and the high signal-to-noise ratio (SNR) of the FLUO module in the same spectral range.  
physical schemes (e.g., [43]) and data-driven statistical approaches (e.g., [10]). Comprehensive reviews of the different SIF-retrieval techniques developed in recent decades can be found in [6; 25; 44].  
In total, four different SIF-retrieval methods are implemented in the HyPlant processing chain, namely Singular Vector Decomposition (SVD), Improved Fraunhofer Line Discrimination (iFLD), Spectral Fitting Method (SFM) and Neutral Atmosphere (NA). The main difference between the SIF-retrieval methods is how they compensate for atmospheric effects, disentangle both the reflected and emitted radiance flux, and accommodate sensor non-uniformities (e.g., spectral shift and SNR). The SVD method is based on a statistical approach and allows an estimation of atmospheric impact from singular vectors. For the remaining three SIF-retrieval methods, different approaches of atmospheric correction have been developed. In the following sections, the different methods are explained in detail.
# Near Infrared Illumination Optimization for Vein Detection: Hardware and Software Approaches
## 2 Methodology
### Vein Image Processing and Morphological Process Enhancement
#### 2.3.1 GrabCut Segmentation for ROI and Cropping  
The GrabCut Segmentation process used iterative graph-based algorithms to disassociate the vein image's background and foreground. The method was designed to resemble a visible ROI. In this study, the GrabCut algorithm [17] using the Gaussian Mixture Models (GMMs) Color Clustering algorithm [18] were used to accomplish segmentation of a selected ROI and concurrently crop the selected ROI. Both strategies were extensively investigated [19]. The following algorithm showed the implementation of the proposed solutions.  
To improve the process, the additional padding that emerges after the segmentation in the initial phase was cropped, leaving just the selected ROI in focus. The procedure for basic image cropping is included in the following algorithm.
## 7 Discussion  
This paper focuses on evaluating the file I/O performance of secure container runtimes. Our evaluation results show that Firecracker offers the highest file I/O performance for all types of operations with high efficiency compared to the other secure container runtimes. Firecracker outperforms the other runtimes in read and write operations by leveraging the virtio block device within the VM for efficient page caching, enhancing the overall performance. In contrast, Kata containers' reliance on a shared filesystem leads to asynchronous request transfers between the VM and virtio-fs, impacting performance. gVisor exhibits significantly lower performance than Firecracker due to its user-space kernel, which necessitates mode transition and system call filtering for each file I/O request, unlike Firecracker's streamlined utilization of the VM's page cache.  
When we determine which runtime to choose for constructing container environments, the security aspect of the runtime should be considered also. As each runtime adopts a different architecture (e.g., the VM layer or user-space kernel) to enhance the security of the containers, the security characteristics can be different. The threat models can be categorized into three cases: (1) protection between a container and an application running in the container, (2) inter-container protection, and (3) protection between containers and the host OS as described in the previous study [47]. The security assessment of each runtime depends on the threat cases. For example, to protect the host OS from malicious containers, Firecracker and Kata containers may be better choices than gVisor. This is because the containers in gVisor execute more system calls (74) to the host OS than those of Firecracker (37) and Kata containers (none) [48; 49]. Moreover, Firecracker and Kata containers assign  
Figure 11: Symbol-level profiling of I/O processing in Firecracker.  
an independent VM to each container, so most of the system calls are handled inside the VM. Therefore, they do not affect the host OS.  
There have been attempts to assess the security of container runtimes [47; 49]. However, previous studies only provide results with limited aspects, such as the evaluation of a single container runtime (i.e., runc) or focusing on a system call-level security assessment. Hence, it is necessary to evaluate in detail the security aspects of secure container runtimes, similar to what we perform for file I/O performance. In future work, we plan to perform security assessments for secure container runtimes and find the trade-off between security and overall performance.
# Robust Hybrid Beamforming Scheme for Millimeter-Wave Massive-MIMO 5G Wireless Networks
## 2 System Model
### Objective Function  
In the traditional digital precoding structure, considering the complexity of the algorithm and the complexity of the mmWave MIMO system itself, the linear precoding algorithm is often used in practical systems. The MMSE algorithm is a commonly used method. The objective function in Equation (9) is obtained to calculate the final precoding matrix:  
\[\min\!E\{\|\mathbf{\bar{s}}-\mathbf{s}\|\}\]  
subject to:  
\[\mathrm{Tr}\ (\mathbf{F}\mathbf{F}^{*})\leq\mathbf{P}, \tag{9}\]  
where \(\mathbf{F}\) satisfies the power-limit requirement of the transmitter, and \(\mathbf{P}\) represents the average power of the transmitter.  
For a hybrid beamforming structure with two stages of processing, by using the Frobenius norm on Equation (3) and taking the trace value of \(\mathbf{F}\), the objective function in Equation (9) can be further written as:  
\[E\{\|\mathbf{\bar{s}}-\mathbf{s}\|^{2}\}=\|\mathbf{\bar{H}}\mathrm{Fr}\mathrm{Fr}\mathrm{ FB}-\mathbf{I}\|_{\rm F}^{2}+\sigma^{2} \tag{10}\]The proposed algorithm is further equivalent to the objective function in Equation (10), and iteratively obtains a hybrid beamforming algorithm with performance close to that of pure digital beamforming.
# Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter
## 5 Verification for Robust Estimation Strategy
### Discussion  
On the basis of the above analysis for the experimental results, we can see that the proposed estimation strategy is superior to the UKF-based one on the performance of no matter accuracy or running time. Besides that, hard bounds containing the real vehicle states are always guaranteed when the ESMF-based strategy is implemented. Therefore, good robustness can be acquired for the dynamic deviation from the reference value. However, the UKF-based estimation strategy cannot achieve this, especially when the vehicle's high maneuver occurs.  
The reason for these phenomena is that the ESMF algorithm inherently treats the distribution of the process and measurement noises as unknown but bounded. When the system uncertainty is included in the bounded noise range defined by algorithm requirement, the ESMF-based strategy performs good ability of robustness. Because it is not sensitive to the process noise representing system modelling errors and the noises existing in measurement signals. However, for the commonly used filtering methods, such as the UKF, the optimal estimation results can only be obtained when the system noise is known as Gaussian white noise. In the experimental conditions set above, the measurement noise does not match the noise assumption for the UKF algorithm. Therefore, there is large deviation for the result of the estimation based on the UKF.
# Function Optimization and Parameter Performance Analysis Based on Gravitation Search Algorithm
## 3 Simulation Experiments and Results Analysis  
### Test Functions  
Six typical functions shown in Table 1 are selected and carried out simulation experiments for the optimal solution under the condition of GSA changeable parameters. There is only one extreme point for \(f_{1}\sim f_{2}\), which is mainly used to investigate the convergence of the algorithm and to test the accuracy of the algorithm. There are many extreme points for \(f_{3}\sim f_{6}\), the difference are in that \(f_{5}\sim f_{6}\) has lower dimension 2 and 4 respectively, while \(f_{1}\sim f_{4}\) are high dimension with 30.
## 3 Results and Discussion
### NPs Interaction with Chalk Mineral in Presence of Hydrocarbon
A similar observation was made with SK7 where, at the lower flow rate (4 PV/day), the dP during NF injection was less than half that from SSW injection. Figure 8: Effluent Ca\({}^{2+}\) and Mg\({}^{2+}\) concentrations for SK5-SSW (**a**), SK6-LSW (**b**) and SK7-mixed (**c**). However, the pressure drop at the higher flow rate was slightly (\(\approx\)0.5 bar) higher during NF injection, with a decreasing trend. The decreasing pressure trend observed in SK7 during 16 PV/day injection of nanofluid was not observed for the same stages in flood SK5 and 6 wherein the salinity of the fluid was constant throughout the experiment, with only addition of NPs. However, in SK7, the fluid salinity is switched from SSW to LSW (with NP). Two mechanisms take place: (1) adsorption of NP on the chalk surface, hence reduced calcite dissolution and (2) disturbance of fluid rock equilibrium due to low salinity. As the injection rate is increased to 16 PV/day, the swept region with LSW + NPs increased, hence reduced pressure. This is evident in Figure 8c wherein at 16 PV/day, a decreasing trend in Ca2+ production was observed. The decrease in Ca2+ may be explained based on reduced dissolution of calcite and dilution factor due to increased sweep. In addition to the main objective, an incremental oil obtained from nanofluid injection in SK5-7 as shown in Figure 10. As mentioned earlier, the recovery here is not optimized to account properly for EOR, but as a matter of observation compared to our previous studies with LSW alone. The incremental recovery was greater in the case of SK6 (0.824%) than with SK5 (0.15%). The highest incremental recovery was observed when the fluid was switched to NF prepared in LSW (1.05% for SK7 experiment). In summary, the results from this work suggests a possible synergistic effect between low salinity flooding and silica nanoparticles wherein adding a relatively small amount of silica nanoparticles to the injected water can improve the flood performance and reduce the risk of disturbing the integrity of the chalk. Seawater has been injected as a primary recovery EOR method in most of North Sea oil fields. LSW injection for enhanced oil recovery could disturb the chalk integrity due to calcite dissolution. This is a major concern for chalk reservoir operators. As outcome of this work by incorporating silica NPs in the injected water encourages applying LSW. The current work is done with model oil in order  
Figure 9: Differential pressure drop (dP) profile for SK5-SSW (**a**), SK6-LSW (**b**) and SK7-mixed (**c**). to investigate the potential for field application. However, as demonstrated here it is dependent on fluid composition i.e., we recommend test related to each individual field fluid.
## 4 Results and Discussion
### Research Results
#### 4.1.1 Establishment of Integrated Evaluation System
Visual harmony, service quality, interaction, media suitability, and pattern recognition are 20.784%, 18.333%, 15.968%, 15.058%, and 14.755%, respectively, which shows that the explained variance in visual harmony is higher than that of the other four indicators; the explained variances in service stability and system response time are 42.007% and 31.006%, respectively, which shows that the explained variance in service stability is higher than that of system response time; the explained variances in access security and privacy security are 36.564% and 36.043%, respectively, which shows that the differences between the explained variances of access security and privacy security are not obvious. Therefore, the scales have good construct validity, and the importance of image restoration function in \"functionality\" is greater than that of the remaining three indicators; the importance of visual harmony in \"ease of use\" is greater than that of the remaining four indicators; the importance of service stability in \"system performance\" is greater than that of system response time; and in the area of \"security\", the importance of access security and privacy security is similar. Overall, the Cronbach's alpha coefficients of all the dimensions in this study are higher than 0.80, while those of the whole scale are as high as 0.952. The cumulative variance is significantly higher, which indicates that the indicators included in each factor in the factor analysis have homogeneity and that there is a high degree of internal consistency among the indicators.
## 4 Discussion
However, ground truth segmentation of skeletal muscle on non-contrast CT is not easy. It is difficult to increase the ground truth easily because it is hard to strictly distinguish tissues such as microvessels and tendons. On the other hand, if the training VP is composed of less than 56 slices, the number of features that can be extracted and the segmentation result will decrease accordingly. However, by reducing the number of images that make up the training VP, there is an advantage of a reduced cost of creating ground truth labels, and the number of learning cases can be increased. It is necessary to examine the change of the segmentation result and determine the number of slices constituting the training VP required for the surface muscle segmentation when taken all together. Furthermore, even when selecting the same 56 slices, a different slice selection method, based on the anatomical position in each case, could be considered (instead of selecting equal intervals). The reason is that slices required for training are not included in the VP slice from the high-density mammary gland in the over-extracted region or abdominal region that were not extracted (Figure 4). Therefore, it is necessary to consider the slice selection method that constitutes the training VP. However, when selecting slices based on the anatomical position, the variation of the surface muscles in each training VP is reduced, so the generalization performance for surface muscle segmentation in regions other than the selected slice may decrease. Therefore, it is necessary to consider the enhancement of training by augmentation. Second, all the CT data used in this study were acquired with the same scanner from Gifu University Hospital. It would be interesting to apply our trained model to CT images from other scanners in order to test the inter-scanner robustness. Considering the fact that unlike MR image values, CT values are correlated with tissue attenuation coefficients, we hypothesize that we can directly apply our trained model to CT data acquired from other scanners. Such a hypothesis needs to be verified in our future work. In addition, although there were no artifacts that affected the segmentation results in the present data, there is a paper that mentions the improvement of segmentation accuracy by reducing metal artifacts in thigh muscle segmentation [21], so there is room for further investigation. Last but not least, the present method was evaluated on CT data collected with a standard clinical protocol. Whether it will work or not on heterogeneous data acquired in clinical routine needs to be further checked in the future. Similarly, since this study was applied only to patients with atrophic muscle diseases, including ALS, it is necessary to verify its applicability to muscle diseases that do not involve atrophy. Although the above issues remain, the proposed method is useful because the surface muscles can be segmented with high accuracy (approximately 90%) from a training VP composed of only 56 slices selected at equal intervals.