esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Description
### Test Procedure  
As described in the scholarly literature, the area replacement ratio refers to the proportion of the area in the cross-section occupied by columns concerning the overall area of the foundations. In practice, stone columns are loaded for an area replacement ratio (A\({}_{\text{r}}\)) varying between 5 and 35% [14]. Utilising a loading plate of 200 mm diameter, individual stone column tests on columns were carried out having diameters of 50, 75, and 100 mm. The observed percentages of A\({}_{\text{r}}\) in the present investigations were 6.25, 14.06, and 25% for columns having diameters of 50, 75, and 100 mm, respectively. The loading plate thickness was determined through an iterative process involving repeated experimentation and analysis. This approach aimed to minimise any observable deformation of the plate under loading conditions. Ultimately, a loading plate with a thickness of 25 mm was selected as it exhibited negligible distortion. This test's loading system was stress-regulated, with the rate of loading controlled with the use of a hydraulic jack that had a capability of 20 kN. The testing procedure involved the application of a monotonic vertical load on the treated clay, followed by the analysis of the load-settlement characteristics of the clay. The end of load application occurred after the settlement in the stone column acquired a magnitude of 50 mm. After the column was constructed, using a plate positioned at the column's centre, the vertical load was applied. When the settlement of the stone column reached 50 mm, the load application was stopped. Single stone columns were subjected to 27 tests in the current investigation. Table 4 provides a comprehensive outline of the experimental tests that were conducted. In all solitary stone column tests, a minimal length to diameter (L/_D_) ratio of 5 was employed, as a minimum value of 4 is necessary to avert bulging collapse [17, 34]. Table 4 and Figure 1 show the various lengths of the encasement used for VESCs and HRSCs.
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 2 AI and Recent RA Enhancements in Literature
### AI for Wireless Networking  
Artificial Intelligence (AI) algorithms act in a way the human mind functions. Cognition and learning are simulated into machines (agents) that interact with their environment to learn meaningful experiences. This is in contrast to the conventional computation paradigms, where algorithms are given a specific set of instructions to operate on inputs to give an output. In AI, however, algorithms start to function from the output and work towards finding the implicit patterns that resulted in the outputs. AI has seen a lot of interesting results in different research domains. The wireless domain is no exception to this.  
A survey of AI techniques is given in [16], where authors discuss how AI can be leveraged to improve the design and operation of future generation wireless networks including 5G. It is discussed that the problems found in the design of these networks are unstructured, and hence AI techniques can be helpful. Some discussed techniques are divided according to the problems found in each layer of the protocol stack. A more detailed discussion and survey about these techniques, however, is found in [17]. As an example of such techniques, [18] proposed a fast machine learning algorithm by modeling the problem as a contextual multi-armed bandit one. Authors in [19] proposed a combination of Support Vector Machine(SVM) and Deep Belief Network(DBN) to solve a joint cross-layered problem: scheduling and power allocation.  
In particular, for deep learning, a subset of AI [20], one can find its application in all layers of the protocol stack including network security (intrusion detection systems) [21]. Deep learning models allow learning from the data they are trained upon. However, in wireless tasks where an agent has to actively interact with the environment and a reward/punishment signal is available, reinforcement learning can be applied. In the recent advancement in the field of AI, deep reinforcement learning is proposed where deep learning is combined with reinforcement learning. The surveys given in [22; 23] discusses deep reinforcement learning applications for wireless networks. For instance, Ref. [24] applied deep reinforcement learning for the resource allocation problem.
## 2 Overview of the Proposed Scheme  
The overall architecture of the proposed scheme is shown in Figure 1. The functions and principles of the scheme are briefly described in this section.  
Figure 1: Schematic view of the proposed scheme.  
The designed scheme is illustrated with the part enclosed by the dashed line shown in Figure 1. Indeed, it consists of two primary functions: (i) voltage profile and static VSM monitoring function, which monitors not only voltage magnitudes but static VSM as well using synchrophasor data from PMUs; and (ii) adaptive secondary voltage control (ASVC) function which provides appropriate control actions to the controllers. The proposed scheme aims to regulate voltage profiles and to enlarge static voltage stability margin by means of a proper adjustment of VAR sources when the power grid has poor voltage profile and/or shows the risk to voltage collapse.  
The flowchart of the proposed scheme is illustrated in Figure 2 and is described in the following:  
Step 1: In this study, we assume that PMUs are installed at the selected pilot buses. Using the precise timing signal provided by GPS as the common time base for PMUs, both magnitude and phase angle of voltage and current signals at different PMU locations can be measured, at exactly the same time instant from all observable system buses. Once the PMU measurements with time tags are entered into the designed scheme, these coherent and real-time measured quantities are then used to process the tasks of the proposed scheme.  
Step 2: At this stage, the proposed scheme performs voltage profile evaluation and static voltage stability margin estimation simultaneously.  
(1)The voltage magnitudes, which are extracted from the PMUs in step 1, are used to evaluate the voltage magnitudes of the monitored buses. As long as the measured voltages are not within the specified limit, voltage violation is confirmed. Then, the scheme will issue a trigger signal to the ASVC function. Otherwise, the scheme will go back to Step 1.  
Figure 2: Flowchart of the proposed scheme.  
2. As mentioned before, depending on bus voltages for the activation of control actions is not a very reliable strategy to be adopted. Another important index, which measures the proximity of an operating point to voltage instability, is the voltage stability margin (VSM). Therefore, the scheme needs to further check to see whether the current power system state is operated within a sufficient safety margin or not. If the computed value VSM is less than the predetermined security margin, inadequate VSM is identified. This will initiate the ASVC action accordingly.  
Step 3: When critical voltage level or inadequate VSM is detected, the ASVC method will be activated automatically. The proposed control strategy can provide appropriate control actions to improve voltage profiles as well as voltage stability margin of the power system. That is, the proposed scheme is capable of preventing the system from possible voltage collapse.  
The following section shows the detailed principles and methods used in the proposed scheme.
## 2 RDFTA (Repairable Dynamic Fault Tree Analysis)
### Reliability Analysis Process of RDFTA  
In the reliability analysis process of RDFTA, the quantitative and qualitative analysis results of the RDFTA are obtained. The existing RDFTA reliability analysis process is not suitable for considering the failure rate and maintenance correlation of repairable systems at the same time. Therefore, using the new RDFTA logic gate, a new RDFTA reliability analysis process framework for repairable systems was proposed, as shown in Figure 6.  
In the next section, the application of 4F integration technology to the reliability analysis of a diesel engine at the design stage is introduced.  
Figure 6: The reliability analysis process framework of the RDFTA method.
# Dynamic Response and Damage Characteristics of Large Reinforced Concrete Slabs under Explosion
## 3 Results and Discussion  
### Dynamic Response Characteristics of the RC Slab  
#### 3.1.1 Explosion Pressure  
Three PVDF sensors were used to determine the explosion pressure reaching the surface of the slabs under different explosion conditions. Typical PVDF pressure-time history curves are shown in Figure 6a. With an increasing TNT charge, the explosion pressures measured by the three PVDF sensors also increased, and the pressure in the middle of the slab was the largest. The maximum explosion pressure with different scaled distances of 0.8-1.26 kg/m\({}^{1/3}\) is shown in Figure 6b.
## 3 Methods  
The ultrasonic measurement method is based on the principle of measuring the time of propagation of the ultrasonic signal on the surface and in the reverse direction. This includes knowledge of ultrasonic signal reflection, refraction and transition [37]. Based on this time, the height of the oil (liquid) level is calculated with the knowledge of the propagation velocity of the ultrasonic signal. Since the propagation velocity of the ultrasonic signal depends on the liquid type, temperature, and pressure, an auxiliary reference surface is used, which is located at a precisely defined distance from the source of the ultrasonic signal. The method of precise measurement consists in sending one ultrasonic pulse (burst) and measuring the time in which this pulse bounces off the reference surface and onto the surface. Based on the knowledge of the reflection time from the reference surface and its exact distance from the ultrasonic signal source, we can precisely calculate the propagation velocity of the ultrasonic signal. This calculated velocity already includes the effects of temperature, density, pressure, and more. The used principle of ultrasonic signal generation and measurement is shown in Figure 1.  
Figure 1: Principle of ultrasonic signal generation and measurement.  
Now, it is possible to define the reflection and transmission coefficients for impacts at different angles--the essence is the vector nature of phase velocity. A Huygens principle is used, represented by Figure 2.
## 4 Discussion
Advanced finite element modeling was employed to evaluate mechanical parameters such as von Mises stresses, deformations, and strain energies, revealing reduced values in the peri-implant bone for platform-switched configurations, which may contribute to mitigating MBL [30]. This protective effect likely underlies the reduced extent of marginal bone resorption observed. However, a concomitant increase in stress concentration was noted in the prosthetic components of the implant system when using platform-switched implants [32]. Further in vitro experimental studies have corroborated these findings, showing increased mechanical parameters in the abutment, implant, and screw within the platform-switched configuration compared to the platform-matched setup. Despite these differences, mechanical complications in the implant prosthetic components were found to be non-significant between the two configurations [30]. The relatively higher stress observed in the abutment does not typically result in premature implant failure, attributed to the robust strength of titanium alloys [32]. Moreover, the lack of significant differences in failure rates between the two platforms substantiates the assertion that platform switching does not detrimentally impact the overall prognosis of the implants [31]. This aligns with the results reported in the present study and may provide valuable insights for clinical consideration. Despite the thorough methodology of our meta-analysis, which incorporated an extensive search strategy and strict adherence to PRISMA guidelines, several limitations warrant acknowledgment. Additionally, the potential for publication bias, as indicated by the asymmetry in the funnel plot, cannot be entirely disregarded, although statistical adjustment methods did not uncover a significant impact. Further research employing standardized protocols and extended follow-up periods is necessary to more clearly elucidate the long-term benefits of platform switching. The clinical implications of these findings must also be carefully weighed. Although the preservation of marginal bone is crucial for the durability and aesthetic success of implant-supported prostheses, the unchanged rates of implant failure suggest that platform switching should not be considered a panacea for implant failures.
# Enabling the Orchestration of IoT Slices through Edge and Cloud Microservice Platforms
## 1 Introduction
### Problem Statement  
Apparently, all the necessary components to provide interoperability in the IoT arena are already present: IoT platforms are supporting multiple customers on the same infrastructure (i.e. multi-tenancy), networks are supporting network slicing, it is possible to deploy functions and applications in different network locations, and lastly, IoT platforms and networks are supporting multiple protocols and types of traffic. However, there is still a key area in the IoT industry where the required interoperability is not providing a complete solution: the automation and orchestration. The components responsible of this orchestration must be able to configure and deploy the network slices and IoT functions (e.g., an IoT gateway) in the proper locations depending on the use case.  
By putting the network slices altogether with the IoT platform multi-tenancy, it is possible to define the IoT slice concept. An IoT slice would then be a partition of the whole end-to-end IoT solution assigned to a specific customer or most probably to a group of customers requiring similar use cases. The orchestration and integration area must embrace this IoT slicing concept.  
Despite of the flexibility provided by 5G networks, they are still not flexible enough to support some specific devices and protocols used in IoT solutions (e.g., ZigBee [5] or 6 LowPAN [6]). In these situations, IoT gateways responsible for translating protocols at different levels will still be needed. These IoT gateways can also be software based, allowing them to be deployed into fog or edge nodes with eventual resource constraints (energy or computational). For instance, a particularly interesting platform to support gateway functions are Small Unmanned Aerial Vehicles (SUAV), which can be flexibly deployed at the network edge to serve to a variety of use cases.
## 4 Structural Models
### Finite Element (FE) Model  
For large or complicated problems, finite element model is typically used. In order to acquire a reasonable FE model, the FE model updating technique is usually adopted to correct the FE model when the FE prediction is in conflict with the test results. One common approach is to consider an objective function that quantifies the differences between the analytical and experimental results, and to minimize the discrepancy by adjusting the unknowns of the FE model [112]. [PERSON] and [PERSON] [113] gave a thorough review on model updating methods using structural dynamic responses. A major issue in FE model updating process is the possible ill-conditioning since there is no unique solution to the inverse problem [114]. There are several methods proposed to deal with this issue, such as the Bayesian approach [115], regularization [110], regularized Lanczos method [116], etc.
## 2 Materials and Methods
### Assessment of Antibiotic Resistance  
The antibiotic resistance was performed following EUCAST recommendations using the disk diffusion test on the Mueller-Hinton (MH) medium (Millipore, Sigma-Aldrich, Darmstadt, Germany). Prepared inoculum with a density of 0.5 on the McFarland scale (approx. cell density 1.5 \(\times\) 10\({}^{8}\) CFU/mL) by suspending colonies of the tested strain in physiological saline. MH plates were inoculated with the prepared suspension and waited until the substrate surface was dry. Then, the following antibiotics were placed on the surface of the plates: Erythromycin (ERY, 15 \(\mu\)g), amoxicillin (AMK, 10 \(\mu\)g), ampicillin (AMP 10 \(\mu\)g), vancomycin (WAN, 5 \(\mu\)g), and tetracycline (OKS, 30 \(\mu\)g). The selection of antibiotics for the experiment was based on an interview conducted at a dairy farm. These were the antibiotics most often used to treat mastitis occurring in dairy cows on the farms from which the milk samples for testing came.  
Strains from the ATCC collection (American Type Culture Collection) were used to control the quality of the assays. As reference materials: _Staphylococcus aureus_-ATCC 29213 and ATCC 25923, for methicillin-resistant _S. aureus_-MRSA ATCC 33591, for _Staphylococcus epidermidis_-ATCC 12228 and _Staphylococcus saprophyticus_ ATCC 15305.
## 3 Theoretical Analysis
### Polar Codes for VLC Channels
#### 3.1.1 Encoding
The noise variance for the \(i\)-th sub-channel, denoted as \(\left(\sigma_{N}^{(i)}\right)^{2}\), is related to the mean value \(m_{N}^{(i)}\) as follows:  
\[\left(\sigma_{N}^{(i)}\right)^{2}=\frac{2}{m_{N}^{(i)}}. \tag{11}\]  
Based on Equations (7) and (11), the Bhattacharyya parameter for the VLC channel is expressed as  
\[Z\Big{(}W_{N}^{(i)}\Big{)}=\exp\left(-\frac{h^{2}}{8\Big{(}\sigma_{N}^{(i)} \Big{)}^{2}}\right)=\exp\left(\frac{-h^{2}m_{N}^{(i)}}{16}\right). \tag{12}\]  
By assessing the Bhattacharyya parameter for each sub-channel \(W_{N}^{(i)}\), we pinpoint the \(K\) sub-channels that exhibit the smallest values of \(Z\Big{(}W_{N}^{(i)}\Big{)}\). The indices corresponding to these chosen sub-channels constitute the set \(A\), which represents the information bits. This methodology underpins the polar encoding strategy tailored for the VLC channel.
# Comparison of Dual-Combustion Ramjet and Scramjet Performances Considering Combustion Efficiency
## 3 Results and Discussion
### Comparison of Overall Performances of the Scramjet and DCR
#### 3.3.2 Comparison of Stream Thrust Functions  
Equation (24) shows that the stream thrust function is a function of the axial velocity and temperature. Due to the interaction between fuel injection, mixing, combustion, and complex shock waves in the initial combustion stage of scramjet, the average flow velocity and temperature gradient change dramatically, and may even show oscillation phenomena. In order to eliminate the effect of this phenomenon on the stream thrust function, a comparative study has been conducted since x = 0.6, as shown in Figure 15b. At the outlet, when the Mach number increases from 6 to 7, the stream thrust function of both DCR and scramjet increases. DCR increased from 2054 m/s to 2194 m/s with an increase of 6.8%; Scramjet engine increased from 1768 m/s to 1999 m/s with an increase of 13%. Compared to scramjet, the DCR stream thrust function is 16% and 10% higher under Ma6 and Ma7 conditions, respectively. It shown that when the Mach number increases, the advantage of the DCR stream thrust function is weakened.
## 6 Discussion
### Capability of the Correction Factors in Equations (12) and (13) to Cover a Broad Range of Cases  
The two correction coefficients in Equations (12) and (13) are developed specifically for the 1:1:2 mixture and under certain pipette-surface contact boundary conditions. Therefore, the coefficients could be used just for the produced 1:1:2 mixture and with equal contact compression force and gap size in which we did the measurements. In future work, the two correction coefficients should be examined for the other three mixtures to investigate the feasibility of using them for a broad range of cases. This could be a further step in developing the pipette aspiration technique in the future. However, developing two unique coefficients for all the four mixture is so challenging. This is due to the high dependency of the two terms of the Equation (1), the generated displacement and excitation pressure, on the boundary condition. As it is indicated in Figure 8, the results depend very strongly on the exact value of the compression force and on the gap size. Therefore, in order to make it possible to develop two unique correction coefficients, the boundary conditions should be exactly equal for all the four mixtures. While uncertainty in measuring equally the pipette-surface contact boundary condition in all the four mixtures is inevitable in the presented experimental setup, the uncertainty comes from the accuracy of measuring the boundary condition. The accuracy should be in the range of tens of (\(\upmu\)m) and hundreds of (\(\upmu\)N) for this purpose.  
Figure 18: Surface plot of the displacement amplitude \(z\)-component in the pipette with gap boundary condition; (**a**) pipette radius 5.25 mm; (**b**) pipette radius 1.25 mm.
## 2 Methods  
Our density functional theory (DFT) calculations are carried out using the Vienna ab initio Simulation Package (VASP) [32; 33]. Projector-augmented wave (PAW) pseudopotentials [34] are used with a kinetic energy cutoff of 500 eV for plane wave expansions. The Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA) [35] is used to approximate the exchange-correlation functional. As validations, the hybrid functional HSE06 is also employed. [PERSON]'s GGA+U approach is employed to address the strong correlation influence on Cr and Mn transition metal atoms, requiring all electronic structure calculations and geometry relaxations to be performed with the spin-polarized DFT+U correction [36; 37; 38]. A U value of 4 eV is used for Cr and Mn, as is typical in the literature [39; 40], for the on-site Coulombic repulsion. The convergence criterion for structural relaxations of the unit cell is \(10^{-6}\) eV for total energies and 0.001 eV\(\cdot\)A\({}^{-1}\) for the force on each atom. A vacuum layer of 25 A along the c-axis, perpendicular to the surface, is utilized to prevent spurious interactions between adjacent periodic slabs. The first Brillouin zone is sampled within a set of 12 \(\times\) 12 \(\times\) 1 and 15 \(\times\) 15 \(\times\) 1 \(\Gamma\)-centered Monkhorst-Pack scheme k-point meshes [41] for structural optimizations and electronic structure calculations, respectively. Spin-orbit coupling (SOC) is included during the electronic structure and MAE calculations. To check the phonon dispersion and the dynamic stability, we perform the density functional perturbation theory (DFPT), as embedded in the Phonopy package [42; 43; 44; 45].