esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Microscopic Interfacial Properties  
Figure 8 presents the SEM images of the specimens at a curing age of 28 d, revealing the microscopic interfacial properties of the control group (10% WFS, water-cement ratio of 1.6) and the specimen with 100% of its natural fine aggregate being replaced by MSWI bottom ash. The observed results revealed that the WFS and C-S-H gel stacked to form a pile. Because the WFS has a large specific surface area, the slurry structure was slightly compact when the WFS content was 10%. This result  
Figure 7: Effect of MSWI content on sulfate attack weight loss.  
verified that the WFS effectively bonded with the slurry, thus increasing the compressive strength of the specimen. Observing the SEM image of the specimen containing 10% WFS and 100% MSWI bottom ash revealed that irregular, rose crystalline phase AFm, flaky CH crystal, and irregular, spike-shaped C-S-H gel were dispersed throughout the specimen. Hydration products were abundant. However, the C-S-H gel was not as intact as that in the control group.
# Systematic Literature Review of Challenges in Blockchain Scalability
## 5 Discussion on Consolidated Paper  
This section discusses the analysis of the 121 selected papers published between 2011 and 2019. It provides insight into the research trend in the last decade on scalability issue and the available solutions for public blockchains. The discussion focuses on the following:  
1. The distribution of blockchain based publications concerning scalability issue over time.
2. The distribution of types of blockchain publication.
3. The distribution of countries of publication.
4. The distribution of application areas of blockchain.  
To appropriately answer the research questions, the data collected during the data extraction process were properly compiled, and the demographic data were analyzed for the mentioned years of publications.  
Figure 4 illustrates the year-wise analysis of the selected papers. The increasing interest of academic research on public blockchain scalability is observed in a rising number of publications over the years. It is noted that majority of the academic research on public blockchains concerning scalability were in 2018-2019.  
There was not much blockchain research until 2015, probably because time was required for blockchain to gain momentum after the launching of bitcoin in 2008. The research on blockchain scalability started to emerge in 2016, specially in public blockchains.  
The research in scalability emerged in 2016, when there were 10 published articles; by 2019 there were over 60, a factor of 6\(\times\) in 3 years. Over these years, blockchain began to disrupt more and more applications on a larger scale. It is therefore logical that the research community should start addressing the burning scalability issue in public blockchains.  
Figure 5 displays the details on the types of publications for the selected articles in this SLR. The following are the types of publications included (and identified) in this study.  
* Journals;
* Conference proceedings;
* Book chapters;
* Workshops;
* Symposiums.  
The findings revealed that most of the publications concerning public blockchain scalability were published in conferences and journals, i.e., 54 papers out of the 121 articles were published in conference proceedings, followed by 33 in journal publications. The remaining articles were in book chapters (20), symposium (8), and workshops (6).  
Figure 4: Figure illustrates the year wise distribution of published paper in the blockchain domain.  
Figure 6 shows the statistics about the geographical distribution of the selected papers. It is noticed that China leads with 24 articles, followed by the USA with 13 papers (published in academia or industries). Moreover, India is the 4 th leading country with eight papers, and closely followed by UK, Switzerland, and Australia with eight, seven, and seven papers, respectively. Only seven countries have over five publications. The rest are below five. This analysis reflects the interest of the research community on blockchain scalability around the world.
## 4 Simulation Experiment and Performance Analysis  
### Simulation Results of Hybrid Source Localization Algorithms  
According to Equations (8) and (9), TOA, TDOA, and TOA and TDOA hybrid source localization are simulated, and the Gauss-Newton method is adopted for the localization iterative algorithm. The simulation results are shown in Figure 2, with the horizontal coordinate as the measurement variance and the vertical coordinate as the positioning error. TOA stations' coordinates are \(S_{4}\)(\(1\times 10^{4}\ 0\) 0), \(S_{5}\)(\(0\ 1\times 10^{4}\ 0\)), and \(S_{6}\)(\(0\ 0\ 1\times 10^{4}\)), the TDOA stations' coordinates are \(D\)(0\(0\ 0\) 0), \(D\)(\(1\times 10^{4}\ 0\) 0), \(D_{2}\)(\(0\ 1\times 10^{4}\ 0\)), and \(D_{3}\)(\(0\ 0\ 1\times 10^{4}\)). Target's coordinate is \(T\)(\(5\times 10^{3}\ 5\times 10^{3}\ 5\times 10^{3}\)). The measurement variance in Figure 2 is the noise variance in Equation (5), which satisfies the zero mean Gaussian distribution. Figure 2a shows the error curves of 3-TOA positioning stations (asterisks), 4-TDOA positioning stations (triangle), 1-TOA and 4-TDOA hybrid positioning stations (circle). Figure 2b,c also corresponds to the error curves of also 3-TOA stations and 4-TDOA positioning errors, while the hybrid positioning error curves are corresponding to 2-TOA/4-TDOA hybrid source localization, and 3-TOA/4-TDOA hybrid source localization. Figure 2c also shows a 3-TOA Cramer Rao Low Boundary (CRLB) curve. Figure 2a-c also show a 3-TOA CRLB Boundary curve. Figure 2d illustrates the spatial distribution of each station.  
It can be seen from Figure 2 that the accuracy of 3-station TOA positioning and 4-station TDOA positioning is essentially the same, which is consistent with the Cramer Rao boundary of 3-station TOA positioning, while the accuracy of hybrid source localization is higher than the individual positioning accuracy of the two methods alone, and the error of the TOA and TDOA hybrid source localization gradually decreases with the gradual increase in TOA stations, i.e., an increase in the amount of TOA stations and TDOA stations can improve the positioning precision.
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 4 Experiments
### Ablation Experiments
#### 4.4.1 The Impact of EGA and CFA  
This section focuses on analyzing the effects of EGA and CFA on the task of meningioma segmentation. ECA mainly contains two kinds of attention, EGA and CFA, and the effects of these two modules on the segmentation accuracy are verified by adding these modules, respectively.  
As shown in Table 5, when only the EGA module is introduced in the meningioma dataset, the model performance is significantly improved, proving its effectiveness in the segmentation task. However, when CFA is to be used on its own, the performance declines significantly. This is due to the fact that CFA is overly sensitive to edges and tends to reinforce many noisy regions as features, leading to poor results. Therefore, it is important to place EGA before CFA because EGA is able to filter out noise feature regions and enhance critical feature regions based on information entropy. This synergy improves the accuracy of segmentation. The advantages of integrating these two modules are also validated by the results on the BraTS2021 dataset. With the BraTS2021 dataset--since it does not have much noise interference--after adding the CFA module alone, the DSC in the WT region rises slightly to 82.03, the DSC in the ET rises to 86.22, and the HD95 decreases to 2.41 in the ET region, indicating that CFA has a certain role in enhancing the segmentation accuracy. After adding the EGA and CFA modules simultaneously, the overall segmentation performance  
Figure 7: Visual segmentation results of models on the BraTS2021 dataset.  
is significantly improved; the DSC values of WT, TC, and ET reach 83.07, 87.04, and 87.15, respectively, and the HD95 values decrease to 3.69 and 2.26 in the WT and ET regions. This indicates that the optimal combination of the ECA module is effective at improving the accuracy and reducing the error.
## 4 Representation and Characterization: Scenarios and States
### S6 Scenario  
As previously mentioned in the preceding section, the modelled systems, as in reality, can exhibit different dynamics depending on other parameters such as space or time. Therefore, it may be that all CSFs vary simultaneously (general case referred to as S7) or that only some of them have gradients, while the remaining ones remain in their previously evaluated states. Furthermore, one might consider the possibility of creating the appropriate conditions for a forced transition from one state to another, needing or wanting to keep some pointers fixed while allowing others to vary. In such cases, it becomes interesting to also consider scenarios from S6 to S0.  
The study of scenario S6 is obtained from S7 by reducing the state space by one dimension.  
Figure 4: Distribution of states in the SE plane. Here the different colors represent different area of interest in terms of Entropy and Energy as written above.  
Figure 5: Relative frequency of the different areas and probability expressed in % of the areas evaluated in terms of the number of microscopic states. Here the different colors represent different area of interest in terms of Entropy and Energy as written above.  
The total number of sub-scenarios is 84. This value is calculated using the well-known expression from combinatorial mathematics for calculating combinations with repetition of n = 4 distinct elements taken k = 6 at a time, which is applicable in the current case  
\[C_{5,6}=\binom{4+6-1}{6}=\frac{9!}{6!3!}=84. \tag{18}\]  
In other words, having a fixed parameter, from a mathematical standpoint, is expressed by reducing the set of elements to choose from by one unit. This means going from the five elements {1,2,3,4,5} to the remaining four, by excluding the fixed one and decreasing the sequence length by one unit, changing from seven to six. Similar to S7, this scenario can also be deconstructed into sub-scenarios containing the same elements except for permutations. In detail, scenario S6 breaks down into the following nine sub-scenarios, taking into consideration their characterization.  
Table 3 shows the number of sub-scenarios and states. However, entropy and energy calculations will not be performed in these cases since the information is incomplete. In other words, the information about which CSF is considered fixed is not known in advance. Additionally, if one wishes to calculate energies and entropies, it would be more interesting to calculate those related to the system's rebalancing. Nevertheless, this is beyond the scope of this section, as it falls under the realm of management strategies for the implementation of an advanced DSS.  
By summing up the states, it is found that the total is 4096, which coincides with the expected theoretical value of 4\({}^{6}\), exactly equal to the number of combinations of four elements taken six at a time (since the seventh element was fixed). However, the previous table possesses greater expressive power, as it subdivides the S6 scenario into sub-scenarios, deconstructing the information.
## 4 Results and Discussion
### Optimization of AuNP Size and Deposition Time to Yield Maximum EC Signal
The LOD also represents the best-case scenario for detecting an analyte on a 3 mm SPCE under our experimental parameters (assuming a near or one-to-one binding of the analyte to an AuNP). Figure 2: Normalized EC peak current (SWV) was obtained for different sizes of AuNPs (5, 10, 20, 40 nm) at different dilutions (D0 = 1.4 \(\times\) 10\({}^{8}\), D1 = 1.4 \(\times\) 10\({}^{7}\), D2 = 1.4 \(\times\) 10\({}^{6}\), and D3 = 1.4 \(\times\) 10\({}^{5}\) AuNPs in 2 uL of DI water). The inset shows the normalized EC plots for dilutions D0 and D1 for clarity. Error bars represent standard deviation (n = 3). A 0.24 uA peak current (n = 3, standard deviation = 0.014) was obtained for the control (2 uL of DI water, no AuNPs). The mean values of normalized current are labeled above the columns. Validation of SARS-CoV-2 Nucleocapsid Protein Binding to Anti-SARS-CoV-2 Nucleocapsid Antibody Utilizing ELISA  
Of the four structural proteins comprising SARS-CoV-2, the spike and nucleocapsid proteins are particularly interesting in viral detection. The spike protein was identified early in the 2019 coronavirus pandemic as a potential viral target. As research on the virus progressed, an RNA-binding protein responsible for genome packaging known as the N protein was identified as one of the most abundant proteins in the virions [35]. The N protein was reported to be of potentially more significant interest as a viral target than the spike protein [36]. This protein has been well characterized by others [37,38]. As per the ELISA data (Figure S3) provided by the manufacturer (ACROSBiosystems), the anti-SARS-CoV-2 capture antibodies used in our work (S46 and S47) exhibited minimal cross-reactivity with the nucleocapsid proteins of other coronaviruses (MERS, HCov-229E, HCov-NL63, and HCov-OC43) [39,40]. The limit of detection (LOD = 3.3\(\sigma\)/S) for the nucleocapsid protein in the ELISA was determined to be 0.075 ng/mL. A plot of this data is shown in Figure S4.
## 5 Applications and Challenges
### Challenges
#### 5.2.3 Preparation Process Challenges  
The fabrication process of DETL is complex and consists of three key aspects: pre-stretching, precise electrode preparation, and lens moulding. Pre-stretching requires control of mechanical uniformity and interfacial bonding; electrode preparation necessitates accurate control of thickness and pattern [113]; and lens moulding presents challenges such as mould surface treatment and liquid injection sealing [114]. Current processes rely on manual operation, resulting in long preparation cycles and poor batch-to-batch repeatability, which limits large-scale efficiency production [115; 116].  
In addition, when integrating a tuneable lens into a camera lens, it is necessary to ensure that the lens and other optical components of the camera can be accurately aligned and maintain a stable relative position during operation. Since DE can deform under the action of an electric field, this may change the relative position between the lens and the other components, thereby affecting the quality of the image.
# Joint Power Charging and Routing in Wireless
## 2 System Model and Problem Formulation  
### System Model  
We consider a WRSN consisting of a single sink node and \(N\) sensor nodes, which periodically generate data with a different rate. The sensor nodes are labeled as \(\mathcal{N}=\{1,...,N\}\). Without loss of generality, each node \(n\in\mathcal{N}\) in the network has an initial energy \(E_{i}^{n}\), a fixed communication radius \(R_{c}\) and generates sensing data at a rate \(r_{i}\). After deployment, we assume all the sensors can be accurately localized via existing localization algorithms, either centralized [27] or distributed [28]. The entire network can be represented as a directed topology graph \(G(\mathcal{N},\mathcal{E})\), where \(e_{ij}\in\mathcal{E}\) if \(d_{ij}\leq R_{c}\), and \(d_{ij}\) represents the distance between nodes \(i\) and \(j\) (\(i,j\in\mathcal{N},i\
eq j\)). We define the node's dead state by its residual energy being below \(E_{min}\). In this state, although it can still sense data, it cannot send its sensing data to the sink node either directly or indirectly via relay nodes, because of the energy constraint.
# Modulated Noncollinear Optical Parametric Amplifier Output Induced by Stimulated Raman Scattering
## 4 Results and Discussion
### Stimulated Raman Processes in Optical Parametric Amplification
#### 4.1.1 Intensity Modulation Structure in the Amplified Signals
These peak positions were recorded and are shown in Figure 7 together with the Raman shifts of BBO indicated by the dashed lines. The vibrational signals in the present study are characterized by their vibrational frequencies. This figure shows that the peaks in the experimental results are correlated with the Raman shift signal of the BBO crystal, indicating that the structures in the NOPA output are partially due to Raman scattering. However, the correlation is not perfect as can be seen in Figure 7.  
Figure 5: Amplified signal intensity as a function of pump-signal delay at four wavelengths. In (**1**–**4**), the curves from top to bottom are respectively taken from Figure 2a–h. Figure 6: Panels (**a–d**) plot the Fourier transforms of the curves in Figure 5(1)–(4). The sequence of curves from top to bottom in each panel follows those shown in Figure 5.
## 4 Discussion
### Apply Short-Term PPG for CEEMD and Entropy Analysis  
Figure 8 showed the time cost of SampEn, CEEMD, and the total time cost. The total time cost is dominated mainly by SampEn. When the sample length is larger than 3000 points, the total time cost is around 2 to 4 s. In addition, five minutes of heartbeats is needed for conventional HRV analysis [1]. These limitations restrict the development of real-time HRV application. Therefore, in this study, only 15 s were recorded for signal processing. However, the output of CEEMD had a boundary effect problem, the first 2.5 s signal and the last 2.5 s signal were eliminated. Total 10 s signal used for iPRV and TSME analysis. The time cost is only about 2 s. Because iPRV breaks the time resolution problem in HRV, the resolution of short-term time series is still enough for time and frequency-domain analysis [11,22]. For non-linear dynamic analysis, the SampEn is sensitive to sample length [17]. Some research suggests that the sample length \(N\) should be larger than 200 points for more consistent findings [29]. However, the effect of multiscale entropy analysis still needs further examination. In addition, the basic algorithm of SampEn, the cost of time is O(n\({}^{2}\)), was applied in this study [17]. Some algorithms can reduce the time cost of SampEn calculation [30]. Moreover, the parameter settings of SampEn and TSME are important. In this work, we follow the same setting as [PERSON], who proposed the TSME [19]. However, it is unknown whether it is the best setting for this study. In future studies, the testing of a different combination of parameter settings is necessary. Research proposed the quadratic sample entropy (QSE) method to dynamically vary the parameter \(r\) to reduce the influence of the parameter [31]. Applying the QSE may be a solution for further study.  
Figure 8: \(Cont\).  
Figure 7: ROC curve of (**a**) pulse rate; (**b**) CDF(E(19)) of iPR; (**c**) logistic model of pulse rate and CDF(E(19)) of iPR.
## 7 Numerical Results
### Optimization Results  
In this section, both single and dual optimization results using a CS algorithm for the examined squat tank isolated either by SFPB (SFPB2 and SFPB3) or TFPB (TFPB2 and TFPB3) are presented. The main results are summarized in Table 2. It can be easily observed from Table 2 that only 39 isolators are derived from the dual optimization process SFPB3 (Figure 7a) compared to the conventional design and sizing optimization approaches, where 60 SFPB are installed at the base of the tank. The requirements regarding the eccentricity minimization and isolators constraints are fully satisfied. Analogously, when the tank is protected via TFPB isolators, the TFPB3 optimization formulation leads to 36 bearings (Table 2 and Figure 7b). It should be noted that for both single and dual optimization schemes, the basic parameters (i.e., \(\mu_{i}\), \(R_{i}\)) can be different for each isolator, while bearings with small differences can be grouped as mentioned earlier. Hence, the displacement capacity of the whole system is bounded by the isolator(s) with minimum displacement capacity.  
In addition, the capacity of each bearing has a direct effect on its cost and, consequently, the total cost of the whole system. Nonetheless, the differences of the bearings' properties in dual configurations (with reduced numbers, i.e., 39 and 36 for SFPB3 and TFFB3, respectively) are small compared to the corresponding ones in the other two schemes with a constant number (i.e., 60). This is verified by the fact that the displacement capacity in Table 2 is the same for the two optimization schemes (0.28 m and 0.72 m for single and triple bearings), while there are small differences from the slightly higher values of the conventional design approach (0.29 m and 0.784 m, respectively). Therefore, the overall cost for the optimized designs derived utilizing the dual optimization process is much smaller compared with those of single optimization and conventional design procedures for both bearing types.  
Figure 8 depicts the convergence histories of the optimization process using a CS optimizer for the different optimization formulations that have been examined in this study. A \"truncated\" plot is also added into the main graph for a better representation of the results. As can be noticed, the combined optimization formulations (SFPB3 and TFFB3) required more iterations to converge compared to the single sizing optimization formulations (SFPB2 and TFFB2) due to the additional complexities of the design space. Regarding the isolator type, TFFB3 required more iterations to converge compared to SFPB3, while TFFB2 converged faster than SFPB2.
## 5 Discussion
### Automated Deployment Platform  
The research presented in this paper showed that an automated deployment would make an important difference in the accessibility of a software environment delivered as a service in the cloud. The resulting platform for automated cloud deployment is written in Node.js for the backend part, and in Angular for the frontend. The platform is designed to deploy multiple kinds of software environments to cloud environments having different providers, with the possibility to add new ones to the list.  
The platform is capable of automating the deployment of the WebGME modeling and metamodeling environment in IBM Cloud, AWS, and Azure. Early work was presented in [38; 39]. Currently, the deployment is realized using technologies such as Docker, Kubernetes, and Helm; they were chosen because they also work well with other important cloud providers, not only with IBM Cloud. To start the deployment, a user is required to register to the platform, create a project, set it as active, and register the cloud connection credentials in the form required for the chosen cloud provider. As future work, the platform will also support the deployment of the Kubernetes service; depending on the chosen cloud provider, the user will have a tutorial on how to deploy a Kubernetes service on the cloud platform.  
Thus, the platform for automated software environment deployment in the cloud provides non-technical users with the opportunity of an easier setup and a fast deployment of the needed software environment in the cloud. It is also possible to create multiple environments for test, development, and production whenever needed. The deployed software environment can also be automatically deleted by the user through the platform. In future work, the platform will support the automatic deployment of other software environments and the deployment of the Kubernetes service on IBM Cloud, Azure, and AWS.