esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Rock-Breaking Mechanism by Spherical Tooth Impact in Granite Formation
## 3 Results and Discussion
### Layout Optimization Research
#### 3.3.2 Spacing Optimization of 22 mm Spherical Teeth  
As shown in Figure 12, when the spacing is less than 51.2 mm, the stress values in the intermediate rock mostly exceed the stress limit, indicating significant strength reduction in the rock. When the spacing exceeds 55.2 mm, a considerable portion of the intermediate rock remains below the stress limit, suggesting that the stress influence is weaker, resulting in a less effective reduction in rock strength. Therefore, considering the balance between reducing cutter spacing costs and achieving effective rock strength reduction, a spacing of approximately 51.2 mm is recommended. At this spacing, most of the intermediate stress values exceed the stress limit, enabling a significant reduction in rock strength, which facilitates rock breakage.  
Figure 11: The influence range of stress under different ring spacing.  
Under the drilling parameters of a 22 mm double-spherical tooth with an impact energy of 35 J and a drilling pressure of 1.4 kN, the optimal cutter spacing and row spacing are determined based on the fracture range and stress influence area. When aiming to achieve a significant fracture range in the intermediate rock, the row spacing is set to 47.2 mm, and the cutter spacing is chosen as 51.2 mm. This configuration effectively reduces the strength of the intermediate rock, thereby enhancing the rock-breaking efficiency.
## 3 Results and Discussion
### Effects of Groove-Induced Anisotropic Wettability and Roughness  
The texturing of the microgrooves on the surface can induce anisotropic properties. [PERSON] et al. (2018) explored the influence of microgrooved surfaces on water droplet repellency. Their CA measurements revealed observable anisotropy between the directions normal and parallel to the groove. [PERSON] et al. (2018) similarly investigated the microgroove effect on the surface wettability and suggested the utility of [PERSON]'s model ([PERSON], 1986) for predicting the CA along the grooves and [PERSON]'s model ([PERSON], 1986) for predicting the CA in the normal direction.  
In the present study, a similar phenomenon was observed on the microgrooved surfaces, where [PERSON]'s and [PERSON]'s wetting states dominated in the directions parallel  
Figure 12: (**a**) Pool boiling curves obtained under test conditions using Novec-7100 as the working fluid; (**b**) HTCs obtained under test conditions using Novec-7100 as the working fluid.  
and normal to the groove, respectively. Therefore, the existence of the wettability anisotropy, as shown in Table 4, can be explained by this difference in the wetting states. [PERSON]'s and [PERSON]'s models are respectively shown by Equations (11) and (12) as follows:  
\[cos\theta_{w}=rcos\theta_{0} \tag{11}\]  
where \(\theta_{w}\) is the contact angle on the rough surface in [PERSON]'s model; \(r\) is the roughness factor, which is defined as the ratio of the real surface area to the apparent surface area; and \(\theta_{0}\) represents [PERSON]'s contact angle for smooth surfaces as follows:  
\[cos\theta_{c}=f_{sl}cos\theta_{0}-f_{lv} \tag{12}\]  
where \(\theta_{c}\) is the Cassie-Baxter contact angle for the rough surface, \(f_{sl}\) is the area fraction of the solid wetted by the liquid, and \(f_{lv}\) is the area fraction of the liquid in contact with the vapor.  
With the groove spacing increasing from 100 \(\upmu\)m to 500 \(\upmu\)m, an enhancement in CA in the normal direction was observed, as shown in Table 4. This groove-spacing effect slightly influenced the CHF values in the boiling heat transfer. Furthermore, the existence of microgrooves on the surface significantly enhances the surface roughness in the normal direction, as displayed in Table 3. With the groove spacing decreasing from 500 \(\upmu\)m to 100 \(\upmu\)m, a smaller groove spacing and more microgrooves can induce more available nucleation sites in the normal direction inside the grooves. This enhancement in the nucleation ability was attributed to the increased surface area provided by more microgrooves on the surface. As a result, groove-induced wettability and roughness variations may influence the boiling heat transfer performance. The effects of the anisotropic properties on the boiling heat transfer are discussed in the following section.
# A Multi-Objective Hybrid BESSA Optimization Scheme for Parameter Extraction from PV Modules
###### Abstract  
In this work, a multi-objective Hybrid Bald Eagle Search Simulated Annealing (Hybrid BESSA) parameter extraction technique for photovoltaic (PV) modules is discussed. First, the efficacy of the Hybrid BESSA was proved via testing on unimodal functions, multimodal functions, and fixed dimensional multimodal functions and the results were compared with the Bald Eagle Search (BES) and other recently proposed optimization techniques. Then, a multi-objective Hybrid Bald Eagle Search Simulated Annealing (Hybrid BESSA) parameter extraction technique was devised for photovoltaic (PV) module parameter extraction. The Hybrid BESSA parameter extraction technique was simulated and analyzed in the MATLAB/SIMULINK environment and in a practical experimental setup for the PV Module AS-M3607-S (G1 CELLS). It was found that the Hybrid BESSA possessed better exploration and exploitation capabilities as compared to the BES and other state-of-the-art techniques. It was found that the fitness function value derived by the Hybrid BESSA technique was less than that of the BES technique when tested under various weather conditions. The percentage error for open circuit voltage, output power, and short circuit current was lower when derived by the Hybrid BESSA in comparison with the BES technique. From the results obtained from modeling the PV Module AS-M3607-S (G1 CELLS) based on Hybrid BESSA-based extracted parameters and BES-based extracted parameters, it was seen that percentage improvement in the combined objective function for the condition of keeping irradiance fixed at 1000 W/m\({}^{2}\) at a temperature varying from \(-\)30 \({}^{\circ}\)C, 0 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 70 \({}^{\circ}\)C were 0.9%, 8.5%, 29.2%, 0.03%, 5.7%, and 0.5%, respectively. When the temperature was kept fixed at 250 \({}^{\circ}\)C and irradiance varied from 1000 W/m\({}^{2}\), 800 W/m\({}^{2}\), 600 W/m\({}^{2}\), and 400 W/m\({}^{2}\), the percentage improvement in combined objective function was found to be 0.5%, 8.1%, 0.5%, and 0.8%, respectively. By analyzing the simulation as well as the experimental results, it was established that the PV model parameter extraction method based on the Hybrid BESSA is more accurate than the BES technique. This analysis is based on a single-diode PV module. A double-diode PV module analysis still needs to be explored.  
Bald Eagle Search (BES); PV module parameter extraction; multi-objective optimization; fitness function; Hybrid Bald Eagle Search Simulated Annealing (Hybrid BESSA); PV module parameter extraction; 27 March 2023
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## 2 Probability Distributions
### Generalized Pareto Distribution (P3)  
The Pareto distribution was introduced by [PERSON] in 1975 [17]. The distribution represents a special case of the five-parameter Wakeby distribution [11; 14], respectively, of the four-parameter Wakeby distribution [13; 17].  
\[\mathrm{f(x)}=\frac{1}{\beta}\cdot\left(1-\frac{\alpha}{\beta}\cdot\left( \mathrm{x}-\gamma\right)\right)^{\frac{1}{\alpha}-1} \tag{20}\]  
\[\mathrm{F(x)}=\left(1-\frac{\alpha}{\beta}\cdot\left(\mathrm{x}-\gamma\right) \right)^{\frac{1}{\alpha}} \tag{21}\]  
\[\mathrm{x(p)}=\gamma+\frac{\beta}{\alpha}\cdot\left(1-\mathrm{p}^{\alpha}\right) \tag{22}\]  
where \(\alpha,\beta,\gamma\) are the shape, the scale and the position parameters; \(\mathrm{x}\geq\gamma\) if \(\alpha<0\) or \(\gamma\leq\mathrm{x}\leq\gamma+\frac{\beta}{\alpha}\) if \(\alpha>0\).
## 2 Materials and Methods
### Protein Analysis of Cell Lysate by SDS-PAGE  
The _E. coli_ strain BL21(DE3), harboring pET21-tri1411 and pET21-tri1411(44), was used for protein expression. Three single colonies were picked and transferred to 2 mL of LB medium and grown overnight at 37 \({}^{\circ}\)C and 220 rpm. The seed culture was inoculated to 100 mL of LB medium at a ratio of 1% (\(v/v\)) and continued to be cultured at 37 \({}^{\circ}\)C. To induce expression, 1 M IPTG was added to a final concentration of 0.3 mM when the OD\({}_{600}\) reached 0.6, and then, it was incubated overnight at 30 \({}^{\circ}\)C. Cells were collected by centrifugation at 4 \({}^{\circ}\)C and 4000 rpm for 10 min and resuspended in 30 mL of phosphate-buffered saline (PBS, pH 7.4). Cells were disrupted by ultrasonication on ice for 30 min. Then, 25 \(\mu\)L of cell lysate was taken as the total protein samples. The cell lysate was centrifugated at 8000 rpm for 30 min at 4 \({}^{\circ}\)C, and the supernatant was determined as the soluble protein fraction. The pellet was then resuspended in 200 \(\mu\)L of PBS, and the resuspension was used as the insoluble protein fraction. The Bradford protein concentration assay kit was utilized for determining the protein concentration in both the supernatant and precipitate, achieving appropriate dilution to ensure equal amounts of protein samples were applied to each lane. All diluted samples were added with 5 \(\mu\)L of 6\(\times\) SDS-PAGE protein-loading buffer, heated at 95 \({}^{\circ}\)C for 5 min, and prepared for SDS-PAGE analysis. The protein concentrations were evaluated by observing the intensity of the bands.
## 2 Materials and Methods
### Measurement and Characterization  
Scanning Electron Microscopy (SEM) images were acquired using the JEOL instrument, model JSM-7610F at 15 Kv. Transmission electron microscopy (TEM) images were acquired using the JEOL instrument, model JEM-1400 at 100 Kv. PerkinElmer Spectrum BX instrument was used to obtain infrared spectra in the region of 400-4400 cm\({}^{-1}\) with a resolution of 4 cm\({}^{-1}\). X-ray photoelectron spectroscopy (XPS) spectra were obtained using the JEOL instrument, model JPS-9030. The UV spectrum was obtained using SpectraMax 384 Plus UV/Vis spectrophotometer with a microplate reader. The surface zeta potential of the polymer-modified MSNs was measured using Zetasizer Nano ZS, Malvern. The specific surface area, pore size distribution, and pore volume of the synthesized materials were characterized using Brunauer-Emmett-Teller (BET) (Gemini VII, 2390 Surface Area, and Porosity USA). Prior to analysis, samples were degassed at 150 \({}^{\circ}\)C under nitrogen flow for 2 h to remove moisture and gasses. The crystalline phase of the fabricated magnetite nanoparticles was determined using powder X-ray diffraction technique, with Mini Flex II X-ray diffractometer equipped with Cu K\(\alpha\) radiation (\(\lambda\) = 1.5405 A) at 5\({}^{\circ}\)/min.
###### Abstract  
Degree attestation verification and traceability are complex one-to-one processes between the Higher Education Commission (HEC) and universities. The procedure shifted to the digitalized manner, but still, on a certain note, manual authentication is required. In the initial process, the university verified the degree and stamp seal first. Then, a physical channel of degree submission to the receiving ends is activated. After that, the degree is attested while properly examining and analyzing the tamper records related to degree credentials through e-communication with the university for verification and validation. This issue poses a serious challenge to educational information integrity and privacy. Potentially, blockchain technology could become a standardized platform to perform tasks including issuing, verifying, auditing, and tracing immutable records, which would enable the HEC, universities, and Federal Education Ministry (FEM) to quickly and easily get attested and investigate the forge proof versions of certificates. Besides, decentralized distributed data blocks in chronological order provide high security between distributed ledgers, consensus engine, digital signature, smart contracts, permissioned application, and private network node transactions that guarantee degree record validation and traceability. This paper presents an architecture (HEDU-Ledger) and detail design of blockchain-enabled hyperledger fabric applications implementation for degree attestation verification and traceable direct channel design between HEC and universities. The hyperledger fabric endorses attestation records first, and then validates (committer) the degree and maintains the secure chain of tracing between stakeholder peer nodes. Furthermore, this HEDU-Ledger architecture avoids language and administrative barriers. It also provides robustness in terms of security and privacy of records and maintains integrity with secure preservation as compared to that of the other state-of-the-art methods.  
blockchain; distributed cybersecurity and privacy; hyperledger fabric; higher education commission (HEC); information security; smart contracts +
Footnote †: journal: Applied Sciences
# An Overview of Emergency Communication Networks
## 5 Sea Ad Hoc Network (SANET)
#### 3.1.5 Comparison of Different Networks  
As shown in Table 3, each emergency communication network technology has its advantages and disadvantages. Satellite communication systems have the advantages of wide frequency band, large communication capacity, low bit error rate, flexible networking, high communication quality, and wide coverage. However, the capacity of satellite communication is limited, the cost is high, and there are communication blind spots. Ad hoc networks have the advantages of flexible networking modes, wide network coverage, self-organization, self-healing, self-balancing, high network reliability, and high scalability. However, ad hoc networks also have disadvantages, such as poor security, limited bandwidth capacity, poor network scalability, and short lifetime. Cellular mobile communication technology has an unlimited communication distance, wide coverage area, and low cost, but its network is unstable and vulnerable to interference. Wireless private network communication technology has high reliability and high security, but its cost is high and communication capacity is small. As a result, in the field of emergency communications, various networks should be used together as complements and cooperate to better accomplish more complex communication tasks.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 6 Impact of Quantum Money on the Banking System
### Comparison with Central Bank Digital Currencies (CBDCs)  
According to the definition, a Central Bank Digital Currency (CBDC) is a digital form of currency issued by a central bank and constitutes a liability of that issuing authority [47; 48]. This subsection aims to compare and contrast the key features of quantum money and CBDCs. Although both seek to modernize the financial system and offer more secure digital payment methods, they differ fundamentally in their operational principles, underlying technologies, and potential impacts on monetary policy.  
CBDCs are digital tokens issued by central banks as legal tender, typically stored in conventional databases or digital wallets. They aim to provide a state-backed alternative to private digital payment systems, offering enhanced payment efficiency, programmability, and traceability. However, CBDCs do not address the core structural issue of symbolic money creation, as they remain classical information objects that can be copied and manipulated within authorized systems. Moreover, in most proposed designs, CBDCs coexist with traditional deposit-based money, leaving the credit-creation function of commercial banks intact [49].  
By contrast, quantum money introduces a physical limit on money creation, as each unit is encoded in a specific quantum state that cannot be duplicated due to the no-cloning theorem. This fundamentally constrains the creation of new currency units, directly tying money supply to quantum physical processes and removing the symbolic flexibility exploited in conventional and even CBDC frameworks. If implemented at scale, quantum money would act not only as a secure medium of exchange, but also as a structural reform tool to prevent the uncontrolled expansion of credit.  
In summary, as presented in Table 1, while CBDCs represent a digital evolution of existing monetary models, quantum money challenges the foundational assumptions of money itself. Its implementation would not merely digitize central bank currency, but redefine monetary sovereignty and structural stability in a future quantum-enabled economy.
## 4 Results and Discussion  
Seven classification algorithms were applied to the datasets, and the results were evaluated based on accuracy, sensitivity, specificity, and precision. Generally, the outcomes were slightly different as each algorithm's working criteria differed. The accuracy of the models was predicted with the help of a confusion matrix, as shown in Figures 4-10. The results showed that the random forest and decision tree algorithms had the best classification results.  
* The classification models are assessed using the metric of accuracy. Formally, accuracy is the percentage of accurate predictions made by our model. The accuracy is defined as shown below [41] and was measured in terms of positives and negatives: \[\text{Accuracy}=\frac{(\text{TP}+\text{TN})}{(\text{TP}+\text{TN}+\text{FP}+ \text{FN})}\times 100\] where TP = true positives, TN = true negatives, FP = false positives, and FN = false negatives
* Sensitivity is a metric that evaluates a model's ability to predict a true positive for each available category. This measure determines the proportion of positive diabetes cases predicted correctly [29] \[\text{Sensitivity}=\frac{(\text{TP})}{(\text{TP}+\text{FN})}\times 100\] where TP = true positives and FN = false negatives.
* Specificity is the metric that evaluates a model's ability to predict a true negative for each available category; it determines the proportion of actual negative cases predicted correctly [27].  
\[\text{Specificity}=\frac{(\text{TN})}{(\text{TN}+\text{FP})}\times 100\]  
where TN = true negatives and FP = false positives.
* Precision is the proportion of true positives to all the positives; it refers to the percentage of relevant results and is a useful metric when false positives are more important than false negatives [27].  
\[\text{Precision}=\frac{(\text{TP})}{(\text{TP}+\text{FP})}\times 100\]  
where TP = true positives and FP = false positives.  
By using the equations above, the performance of the various classification models can be compared, as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy\%** & **Sensitivity\%** & **Specificity\%** & **Precision\%** \\ \hline K-nearest & 92.39 & 94.44 & 77.27 & 90.00 \\ Neighborts & 96.74 & 98.68 & 87.88 & 83.71 \\   machine; & 96.74 & 98.10 & 88.46 & 87.08 \\   Naive Bayes & 98.37 & 100 & 92.11 & 80.66 \\   Decision tree & 98.37 & 98.01 & 84.85 & 84.1 \\   Random forest & 96.19 & 98.71 & 82.76 & 86.44 \\   Linear & & & & \\   discriminant & 97.3 & 93.33 & 97.96 & 93.9 \\   analysis & & & & \\ Artificial neural & & & & \\   networks & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance results.
# Applying Deep Learning to Medical Imaging: A Review
## 3 Applications in Medical Imaging
### Image Reconstruction  
#### 3.3.1 Techniques and Approaches  
Image reconstruction is a fundamental step in many medical imaging modalities, such as CT, MRI, and PET, where raw data (e.g., projections, k-space data) are transformed into interpretable images [120]. DL has shown potential in improving image reconstruction quality and reducing reconstruction time [121]. CNNs have been used for image denoising, super-resolution, and artifact reduction in various imaging modalities [122; 123]. Additionally, DL-based iterative reconstruction techniques [124] and the integration of DL models with conventional reconstruction algorithms [125] have been proposed to optimize image quality while reducing radiation dose or acquisition time. Figure 7 presents the application of GAN-based PET image reconstruction.
## 2 Materials and Methods
### Quantification of the EOS Trends, Turning Points, and Controls  
After extracting the EOS at the pixel scale, we first quantified the tendency of the EOS changes using greenness changes methods, and then detected the turning points using the piecewise regression method. The mean EOS values and EOS trends within the subregions were calculated as the EOS and EOS trends at the subregion level. The turning points at a subregion and province level were calculated by the majority values.  
The EOS trends were calculated using the greenness rate of change [41]. The EOS was considered delayed if the slope was a positive value; otherwise, the EOS advanced.  
\[slope=\frac{n\times\sum_{i=1}^{n}(i\times NDVI)-\sum_{i=1}^{n}i\sum_{i=1}^{n} NDVI_{i}}{n\times\sum_{i=1}^{n}i^{2}-(\sum_{i=1}^{n}i)^{2}} \tag{4}\]  
where \(i\) is the order of the year, \(n\) is the number of years, \(NDVI_{i}\) is the \(NDVI\) in the \(i^{\text{th}}\) year, and the slope is the vegetation change rate. Alternatively, we can use the unary linear regression, in which the \(P\) values and confidence levels can be calculated.  
Turning points were identified by piecewise regression [42] analysis, as defined in Equation (5), which can be used to detect sudden and sharp changes in directionality. This method has been widely applied for analyzing vegetation dynamics [19; 43; 44].  
\[y=\left\{\begin{array}{c}\beta_{0}+\beta_{1}t+\varepsilon\,t\leq\alpha\\ \beta_{0}+\beta_{1}t+\beta_{2}(t-\alpha)+\varepsilon\,t>\alpha\end{array}\right. \tag{5}\]  
where \(t\) is the order of the year, \(\alpha\) is the estimated turning point of the vegetation change trend determined using the least square error method, \(\beta_{1}\) and \((\beta_{1}+\beta_{2})\) are the change rates before and after the turning points, respectively, and \(\varepsilon\) is the residual error. We performed \(t\)-tests to check the significance of the piecewise regressions.  
Redundancy analysis (RDA) is a powerful analysis technique that could be applied in separating the contributions of climate, human activities, and their intersections to the EOS changes. RDA is a method to extract and summarize the variation in a set of response variables that can be explained by a set of explanatory variables [45]. In this study, RDA was performed with the vegan package in R language [46]. In RDA, climatic variables or human activity variables were chosen as predictors to maximize the extent of their correlation with the EOS changes as the response variable. RDA had been widely used in ecology-related studies [47; 48]. The turning points of human activities were also calculated with Equation (5). The relationships between the turning points of the EOS and climatic variables were quantified using partial regression analysis or the correlation coefficient.