esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Basic Concrete Properties  
The slump and mechanical properties (\(f_{\rm c}\) and \(E_{\rm c}\)) of the concretes are shown in Table 4. The compressive strength and modulus of elasticity values are derived from the stress-strain curve. The chart shows the average of the three specimens with the coefficient of variation (CoV) in parentheses (in %).  
Figure 7 shows the compressive strength experimentally obtained for all the concretes. Within concretes made with the same aggregate, the influence of fibers is relatively minor. When considering a constant fiber content, the average compressive strength of the parent concrete is the highest, reaching 31.2 and 34.4 MPa for parent concretes with 3 and 9 kg/m\({}^{3}\) of fibers, respectively. For the concretes with RF, the compressive strength is reduced to 6.4% and 18.0% for 3 and 9 kg/m\({}^{3}\) of fibers, respectively. This finding can potentially be attributed to improper fiber distribution of RFs due to their shape, resulting in the balling of fibers and air pockets, which adversely affect the compressive strength of concrete [30; 31]. Therefore, the recovered fiber has a negative effect on the compressive strength. For the RA, the compressive strength was also weaker than the NA (parent concrete), even though the cement content was increased. Nonetheless, it was shown that a target mean strength of 30 MPa can be achieved. The strength of the concrete with RA was not significantly affected by the RA type (R3 or R9).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Concrete** & **Slump (mm)** & \(f_{\rm c}\) **(MPa)** & \(E_{\rm c}\) **(MPa)** \\ \hline N-3 & 134 & 31.2 (1.8\%) & 28,449 (2.4\%) \\ N-9 & 107 & 34.4 (0.3\%) & 31,253 (5.7\%) \\ \hline RF-3 & 135 & 29.2 (1.2\%) & 28,983 (5.0\%) \\ RF-9 & 115 & 28.2 (11.7\%) & 27,479 (2.1\%) \\ \hline R3-3 & 143 & 30.9 (4.8\%) & 19,627 (11.5\%) \\ R3-9 & 120 & 28.3 (0.7\%) & 18,147 (5.0\%) \\ R9-3 & 102 & 27.1 (2.7\%) & 21,772 (8.4\%) \\ R9-9 & 114 & 30.4 (5.5\%) & 21,270 (12.6\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The concrete basic properties.  
Figure 7: The compressive strength result of tested concrete.  
Figure 8 shows the elastic modulus of all the concretes. The parent concrete is similar to RF, increasing by 1.75% and decreasing by 12.1% at 3 and 9 kg/m\({}^{3}\), respectively. However, for the RA, the modulus of elasticity underwent a significant loss when compared with PC, around 30% for both R3 and R9. Nonetheless, this is in line with findings in the literature on the effect of RA on the modulus of elasticity [30].
## 3 Hsp60 in Carcinogenesis
### Hsp60 in Extracellular Vesicles in Carcinogenesis
MicroRNAs transported by EVs are being considered the main players in the modulation of target cell functions [135; 136]. Therefore, tumor-derived EVs are attracting attention from scientists interested in molecules that might be used as biomarkers for diagnosis and patient follow up. In addition, EVs are candidates for delivering anti-cancer compounds to specific target tissues because their content can be modified; they show some tissue-specificity; and their immunogenicity is low when they are isolated from the same patient, thus presenting slow risk of generating anti-EV immunity [137; 138; 139]. Our research group provided the first evidence that tumor cells actively release Hsp60 via exosomes, through a secretion mechanism that requires the translocation of the chaperonin molecule to the plasma-cell membrane and its association with lipid rafts (Figure 2) [47; 48]. The exosomal release of Hsp60 by human tumor cells can be enhanced by anti-cancer treatment. For instance, histone deacetylase inhibitors SAHA causes cell cycle arrest and death in a human lung-derived carcinoma cell line; this cytotoxic effect was associated with generation of oxidative stress, mitochondrial damage, and diminution of the intracellular level of Hsp60, which became nitrated and was released via exosomes [110]. It has been suggested that the released Hsp60 interacts with the immune system, generating an anti-tumor response that potentiates the effect of SAHA. In this manner, exosomal Hsp60 could modulate the tumor microenvironment and, from the practical standpoint it could be consider for use as a diagnostic and prognostic biomarker [123]. Indeed, it was found that the plasma levels of exosomes with Hsp60 diminished after anti-cancer treatment that caused tumor mass reduction in bowel cancer and thyroid papillary carcinoma [123; 100]. Additionally, treating hepatocellular carcinoma with anti-cancer drugs caused the release of a larger amount of exosomes containing Hsps, including Hsp60, which was followed by an enhanced anti-tumor immune response mediated by natural killer cells [121].
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 3 Cloud Security Frameworks
Organizations can use CSA STAR to assess and compare various cloud providers before making informed decisions. 7. ENISA Cloud Security Guide:  
The ENISA Cloud Security Guide offers practical advice for enhancing cloud security. It covers a wide range of cloud security topics, including risk assessment, governance, compliance, and incident response. This guide is valuable for organizations seeking comprehensive insights into cloud security considerations. 8. CIS Controls for Cloud:  
The Center for Internet Security (CIS) Controls for Cloud provides a set of best practices for securing cloud environments. It focuses on critical security actions that organizations should implement to protect cloud resources. These controls cover areas such as identity management, data protection, and vulnerability management. Cloud Controls Matrix (CCM):  
The Cloud Controls Matrix (CCM) by Cloud Security Alliance offers a comprehensive framework for assessing the security posture of cloud services. It maps security controls to various industry standards and regulations, providing a detailed reference for evaluating cloud provider security. 10. CSA Security Guidance:  
The Cloud Security Alliance (CSA) Security Guidance offers a set of best practices and recommendations for securing cloud environments. It covers a wide array of cloud security topics, including governance, risk management, compliance, and encryption. This guidance is valuable for organizations seeking to build a robust security foundation in the cloud. These frameworks are used to improve security by both cloud service providers and cloud clients. Cloud security frameworks are widely recognized and used across a wide range of sectors. Organizations use these frameworks to analyze cloud providers' security postures, review their own security procedures, and create confidence with customers and stakeholders. Compliance with well-known frameworks contributes to the development of a common language and knowledge of cloud security standards. This chapter gives an overview of the cloud security framework, its implementation process, and the importance of putting one in place. The frameworks we will focus on are:  
1. COBIT 5 (Control and Assurance in Cloud: Using COBIT5)
2. NIST (National Institute of Standards and Technology)
3. ISO 27017 (International Organization of Standardization)
4. CSA STAR (Cloud Security Alliance Security, Trust, and Assurance Registry)
5. AWS Well-Architected Framework  
These five frameworks cover a range of perspectives, from offering a structured approach to aligning IT with business goals (COBIT5) then comprehensive cloud security considerations (NIST SP800-144) to specific security controls (ISO 27017)and practical implementations (AWS Well-Architected Framework and CSA STAR). Also a short comparision of these 5 frameworks with their focus, scope, and approach are in the Table 4.
# Expanding Moment Magnitude Pools for Earthquake Magnitude Homogenization
## 2 Materials and Methods
### Magnitude Homogenization  
The general procedure for magnitude homogenization is to estimate a relationship between a target magnitude type from a pool of other magnitude types. To do so, regressions are made from the compiled earthquake catalogs for M\({}_{\rm W}\) against other magnitude types. For this study, ordinary least squares regression will be applied, with a parametric form of:  
\[{\rm M}_{W,\rm proxy}=\beta_{1}\times{\rm M}+\beta_{0} \tag{1}\]  
where \(\beta_{0}\) is the intercept, \(\beta_{1}\) is the slope, and M is the magnitude type from an agency of interest.  
Although much of the literature purports the advantages of orthogonal type regressions [1; 3; 25], many of these presentations assume equal measurement variance to perform their regressions as such uncertainty parameters are typically not available in the input magnitudes. Several studies suggest the a priori assumption of equal uncertainties is not true [7; 11; 26; 27]. Moreover, the resultant regressions would suggest the input also be a random variable, which makes use of such regressions impractical. This is not to say that ordinary least squares regression is better, but that the impact of varying M\({}_{\rm W}\) pools should not be significantly affected by regression type. Interestingly, at least one study on magnitude homogenization comparing linear and orthogonal regressions showed similar results when there were few outliers [4].
## 1 Introduction
Although the conceptual design and structural efficiency can be improved by using the above approaches, these concepts also lead to manufacturing and testing problems. In particular, [PERSON] et al. [27] presented the use of the honeycomb sandwich structure in the metallic TPS panel, which could be used under a high temperature of 1073 K. However, welding of a honeycomb core to  
Figure 1: The thermal protection system (TPS) panel designed from our previous study [6]. (**a**) Side view of the model, (**b**) the fabricated TPS panel, (**c,d**) a support bracket with washers at a corner. the face sheets is a big challenge. Hence, what remains to be developed is a novel lightweight sandwich structure with a corrugated core that takes advantage of easy manufacturing. [PERSON] et al. [18] reviewed the high performance of the energy absorption capacity of the bio-inspired sinusoidal corrugated core sandwich structure under quasi-static loads. [PERSON] et al. [28] presented a bio-inspired conical corrugated wall for high energy absorption. [PERSON] et al. [29] studied the design of a developable double corrugation surface that could be employed for transformable structures. [PERSON] et al. [30] presented the influences on mechanical properties of a zigzag-like foldcore sandwich structure in a compression test. However, the use of the bio-inspired corrugated core sandwich structure under extreme thermal loads is very rare and thermomechanical characteristics have not been identified yet. To address these challenges, we proposed a modified design of a bio-inspired sinusoidal corrugated core sandwich structures and investigated their thermomechanical performance under high-temperature conditions. Here, we present the bio-inspired sandwich structure as a lightweight, high-strength structure for the outer structure of the TPS panel. The corrugated core of the sandwich panel was inspired and modified from the microstructural shape of the _Odontodactylus scyllarus_ shrimp's dactyl club, which is shown in Figure 2a,b. Photos are reproduced from the work of [31]. The sagittal section of the impact region of the dactyl club was observed by computerized tomography scan, as shown in Figure 2c. Higher magnifications of the sinusoidal shape are shown in Figure 2d,e. A schematic of the geometry for the sinusoidal shape is shown in Figure 2f. This paper aims to propose the corrugated core sandwich structure panel to reduce the weight and guarantee the strength for the TPS panel under high-temperature conditions. We developed two types of corrugated core for the sandwich structures: the unidirectionally corrugated core sandwich structure (UCS) and the bidirectionally corrugated core sandwich structure (BCS). The study investigates the deflection of the two sandwich structures for typical operating trajectories of the hypersonic vehicle and determines whether the resulting deflection was acceptable for the TPS panel. In addition, we compared the two sandwich structures in terms of the thermal stress and the thermal deflection limits. Furthermore, we investigated the parameter effects of the sandwich structures on the thermomechanical performance.
## 3 Results and Discussion
### Experiments in Flight Conditions
#### 3.2.2 Comparison of the Tuning Effectiveness: FIB vs. GLD Method Used in Real-World Conditions  
In the Figure 11, the altitude controller gains during auto-tuning procedure using GLD and FIB methods, are presented. Based on simulation results it was decided to terminate both methods after 48 iterations. Final and average values of \(J\) (see Figure 12), are lower for the GLD method: 43.97% and  
Figure 10: Tracking of the reference altitude \(Z_{set}\) by the Bebop 2 UAV in three trials (\(Z_{1}\)–\(Z_{3}\)).  
Figure 9: Time courses for: (**a**) first two iterations of the GLD method (mistuned gains), (**b**) last two iterations (well-tuned gains) for \(Z_{GLD1}\) (\(\alpha\) = 1.0, \(\beta\) = 0.0), \(Z_{GLD2}\) (\(\alpha\) = 0.9, \(\beta\) = 0.1), \(Z_{GLD3}\) (\(\alpha\) = 0.8, \(\beta\) = 0.2).  
3.2.3 Analysis of the Impact of Environmental Disturbances (Wind Gusts) on the Auto-Tuning Procedure  
Usually in scientific world literature presented results of research on UAV flights under wind gust conditions concern the case when the air stream is directed towards the UAV frontally. In real flight conditions, this direction is usually random and variable in time. Thus, it was decided to verify the effectiveness of the GLD auto-tuning method with a low-pass filtration, during the UAV flight, in the stream of the air generated from the rotating fan (1.2 m high), at a distance of 1.8 m, behind the UAV on the left, as in Figure 14.  
In the auto-tuning procedure, the disturbances were introduced twice (see Figure 15). In the first phase, the maximum air flow speed was 2.7 m/s, in the second--3.7 m/s. It is a severe disturbance referring to the ratio of physical dimensions to the small weight of the UAV. A complete 56-iterative tuning cycle was conducted. The results are summarized in Figure 16 and Table 11 (see Appendix A), and compared with the results of the auto-tuning from the previous Subsection. Very similar, promising final values of the \(J\) performance index were obtained--even only slightly smaller for the case of impact of a wind gust during the GLD procedure.  
Determinism of the method is illustrated by the results of 10 first iterations in both trials and iterations no. 29-38, where for different values of \(J\), the calculated \(k_{P}\) gain values are identical. Similar behavior can be observed in the presence of wind gusts (iterations no. 15-20, and 43-48). In the future research, it is worth considering an approach in which two or several UAV units (agents) could be used to parallel measurements and averaging computations during the auto-tuning procedure, resulting in better tuning precision.  
Figure 14: Test bed for research on wind gusts impact on the GLD method.  
Figure 15: Time course for the GLD tuning process in the presence of wind gusts.
## 2 Data and Methods
### Methods
#### 2.2.3 Wind-Based Upwelling Index  
This paper aims to explore the impact of the wind field on upwelling in the central and western South China Sea by evaluating the upwelling index using wind field conditions. The Ekman transport refers to the horizontal fluid motion caused by the combined action of the surface winds and the Coriolis force. According to the Ekman transport principle, the intensity of the upwelling, WUI, is defined as the Ekman transport per unit length perpendicular to the coastline [22].  
\[\left\{\begin{array}{l}WUI=\sin(\theta)Q_{x}-\cos(\theta)Q_{y}\\ (Q_{x},Q_{y})=\frac{1}{\rho_{w}f}(\tau_{y},-\tau_{x})\end{array}\right. \tag{3}\]  
The angle, \(\theta\), represents the angle between the landward side of the coastline and the due east direction, with the value of \(\theta\) varying between \(29^{\circ}\) and \(122^{\circ}\) depending on the angle of the eastern coastline of the Central South Peninsula. \(Q_{x}\) and \(Q_{y}\) represent the latitudinal and longitudinal components of the Ekman transport, respectively. They characterize the directional components of the Ekman transport flow on the Earth's surface, which is closely related to the formation of wind-driven upwelling. A positive (negative) value of WUI indicates favorable (unfavorable) wind field conditions for upwelling. The seawater density \(\rho_{w}\) in the study area is 1.025 \(\times\)\(10^{3}\) kg/m\({}^{3}\). The wind stress component in the meridional direction is represented by \((\tau_{x},\tau_{y})\), and \(f\) denotes the Coe-style parameter [23].
## 3 Distributed Jamming System Model for LEO Constellation Downlink  
By identifying and separating the signals of LEO satellites and incorporating the positional information of distributed jammers and the satellite constellation, a LEO satellite downlink distributed jamming system model was constructed. The system consists of the LEO satellites, jammers, and ground jamming targets, as shown in Figure 1, where \(J_{N}\) represents the \(N\)th distributed jammer, \(S_{k}\) represents the \(K\)th LEO satellite, and \(U_{M}\) represents the \(M\)th ground jamming target. The green lightning-like lines represent communication links between LEO satellites, yellow lightning-like lines represent communication links between onboard distributed jammers, orange dashed lines represent the interception links of onboard distributed jammers to signals from LEO satellites, blue dashed lines represent the beam coverage of LEO satellites to ground targets, and red solid lines represent the beam coverage of onboard distributed jammers to ground targets. The Maseng-Bakken model (MBM) [37; 38], which assumes that the rate of change in rain attenuation is proportional to its instantaneous value, was adopted as the downlink channel model.  
All distributed jammers were equipped with satellite navigation and communication receivers, providing the possibility for these jammers to collaboratively network with each other.  
Based on the identification and localization of each satellite within the region, and utilizing the positional information of jammers in the satellite-to-ground link domain, a multi-objective jamming resource scheduling model was constructed, as illustrated in Figure 2. Considering the different deception strategies of distributed jammers in the power, time, and frequency domains, the optimization and coordinated control of distributed deceptive jammers are implemented. This allows for coordinated jamming in the satellite-to-ground links of multiple LEO satellites within the region, disrupting the downlink communication network of the LEO satellite constellation.  
Figure 1: Topological structure of the LEO satellite downlink distributed jamming system.
## 7 Discussion, Limitations, and Future Research Trends
### Future Research Trends  
1. It has become more important to address the real-time requirements for OD in real-world applications. Deep neural networks, however, frequently require large amounts of computational power, which presents difficulties for embedded systems. To properly fulfill these objectives, resource-efficient technique development has become essential. To ensure the practical usefulness of the suggested methodologies, future research should focus heavily on their computational components, offering both quantitative and qualitative analysis.
2. The existing deep neural network techniques for difficult item detection mainly depend on large-scale annotated datasets. However, creating these databases is expensive and time-consuming. Consequently, there is an urgent need to create OD algorithms that can train with little to no labeled information.
3. The employment of various evaluation metrics and IoU criteria for OD in difficult situations has resulted in the absence of a clear benchmark standard, which makes comparing methods difficult. For future research in this area to be uniform and comparable, a global baseline must be established.
4. Creation of extensive simulation environments should occur that imitate inclement weather to thoroughly test and improve object identification algorithms.
5. To develop comprehensive solutions for adverse weather OD, researchers, engineers, and policymakers should collaborate more closely.
6. It is necessary to study the psychology and behavior of human drivers in adverse weather, with an emphasis on developing successful communication and trust with AVs.
7. Creation of novel tactics for the real-time modification of OD algorithms for AVs in response to changing environmental circumstances should be achieved.
8. Investigation into cutting-edge techniques for combining current weather data with weather forecasts to enable proactive decisionmaking during unfavorable weather conditions is necessary.
9. Improvements in sensor fusion methods should be attained, which integrate information from several sensor types to provide more accurate and dependable identification in adverse weather.
10. To develop behavior prediction models for AVs, leveraging machine learning and deep learning to forecast the actions of vehicles, pedestrians, and cyclists should occur. These models will operate effectively in adverse weather, improving AV decisionmaking for enhanced safety and efficiency.
# Hemodynamic Analysis on the Anastomosis Angle in Arteriovenous Graft Using Multiphase Blood Model
## 3 Numerical Details
### Geometry and Numerical Detail  
The simplified geometry of the arteriovenous graft was generated in a circular shape. Since the physiological curvature of blood vessels form anisotropic flow, the shape closest to the circular shape among the CT data was selected for ease of flow analysis, as shown in Figure 3.  
The diameters of the vein and artery were set as 7 and 3.8 mm, respectively, considering the minimum diameter measured on ultrasound imaging among the patients (Table 1). To investigate the effect of the anastomotic angle (\(\theta_{\mathrm{g}}\)), the angles were set to \(15^{\circ}\), \(30^{\circ}\), and \(45^{\circ}\) (Cases 1, 2, and 3, respectively) based on the patients' computed tomography data.  
The boundary conditions of the graft and vein inlet were set based on the Doppler data obtained throughout the cardiac cycle, as shown in Figure 4. The velocities of the graft and vein inlet were measured at the graft and before and after the venous anastomosis. After calculating the velocity by using fast Fourier transforms, the mean minimum peak systolic velocities of 1 and 0.3 m/s were used for the boundary conditions of the artery inlet (A). The hematocrit of the artery inlet was 34% in all cases. The zero gradient with mass rate weighting is applied at the boundary condition of the outlet (E,F). Accordingly,  
Figure 3: Geometry and boundary conditions of AVG. Artery inlet; Hematocrit: 34% (**A**), Suction needle (**B**), Injection needle; Hematocrit: 34%, 45%, 58% (**C**), Anastomosis angle; \(15^{\circ}\), \(30^{\circ}\), \(45^{\circ}\) (**D**), Venin outlet (**E**), Artery outlet (**F**).  
Figure 2: Viscosity distribution with hematocrit.  
the mass rate weighting of the vein outlet was set to 100%. The flow rate of suction and injection needle were \(-400\) and 400 mL/min (B,C), respectively. Hematocrit introduced into the graft through the injection needle was 34%, 45%, and 58%. The blood vessels were assumed as rigid bodies with nonslip conditions.  
The maximum Reynolds number of the flow was larger than 120,000 in all the cases, and the standard k-\(\varepsilon\) model was used to consider turbulence. The numerical studies were performed using commercial software (ANSYS Fluent v19). The PISO scheme was used for pressure-velocity coupling. In addition, the second-order upwind scheme was used for the discretization of the momentum. The convergence criterion of the residual set was 0.001 for mass continuity. The computational time was approximately 20 min for each case on a 16-node 3.2-GHz CPU.