esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Probabilistic Semi-Explicit Cracking Model for Concrete Structures
## 2 Development and Implementation of the 3D Model
### Probabilistic Model Formulation
The initiation of the elementary dissipative process occurs when the maximum principal stress (\(\sigma_{1}\)) at a specific Gauss point reaches the randomly assigned material tensile strength (\(f_{t}\)). Subsequently, when the total available energy for the finite element is dissipated, the element is considered cracked, and its elementary stiffness matrix is then nullified ([PERSON], 1954). This dissipation mechanism is mathematically expressed through an isotropic damage law, as depicted in Equation (1), where \(\varepsilon_{0}\), \(\tilde{\varepsilon}_{fi}\), and \(\tilde{\varepsilon}^{k}\), respectively, denote the damage initialization strain, the maximum critical strain, and the equivalent strain. \[D=1-\frac{\varepsilon_{0}}{\tilde{\varepsilon}^{k}}\bigg{[}1-\frac{\left( \tilde{\varepsilon}^{k}-\varepsilon_{0}\right)}{\left(\tilde{\varepsilon}_{ fi}-\varepsilon_{0}\right)}\bigg{]} \tag{1}\]  
The maximum critical strain is defined as \(\tilde{\varepsilon}_{fi}=\frac{2g_{IC}}{f_{t}}\) and the equivalent strain is evaluated according to Equation (2), where \(\left\langle\varepsilon_{i}\right\rangle_{+}\) is the positive part of the main strain in the \(i\) direction, which means that \(\left\langle\varepsilon_{i}\right\rangle_{+}=\varepsilon_{i}\), if \(\varepsilon_{i}\geq 0\); otherwise, \(\left\langle\varepsilon_{i}\right\rangle_{+}=0\). However, for a state of uniaxial tension in the direction 1, this expression is reduced to \(\tilde{\varepsilon}^{k}=\varepsilon_{1}\). \[\tilde{\varepsilon}^{k}=\sqrt{\sum_{i=1}^{3}\langle\varepsilon_{i}\rangle_{+ }^{2}} \tag{2}\]  
The stress-strain relationship of the material during a stage of damage can be expressed in relation to the undamaged stress-strain relationship, as depicted in Equation (3). Here, \(\tilde{E}\) and \(E_{0}\) represent the elastic modulus of the damaged and undamaged material, respectively, while \(D\) denotes the damage variable. \[\sigma=\tilde{E}\varepsilon,\quad\tilde{E}=E_{0}(1-D),\quad(0\leq D\leq 1) \tag{3}\]
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 3 An In-Depth Analysis of the mmWave Hybrid Beamforming Landscape
### Network
This is the primary justification for considering small-cell distributed mixed analog-digital processing [88]. * **Cloud radio access Network** It is well recognized that hybrid beamforming provides a simple solution to issues encountered in systems with several antennas. In a downstream cloud radio access network (C-RAN) architecture, the analog and digital components of the hybrid beamforming method for remote radio heads (RRHs) function well. \"Cloud\" computing uses a baseband processing unit (BBU) to carry out digital operations. Before being sent to the RRHs across the restricted capacity fronthaul connections, the precoded baseband signals are quantized. This approach takes into account two distinct channel state information (CSI) scenarios: (1) the optimal CSI at the BBU and (2) the fact that CSI does not necessarily work [89]. Finding the optimal fronthaul quantization techniques and digital beamforming at the BBU, as well as the optimal analog radio frequency (RF) beamforming at the RRHs, requires considering the impact of quantization noise on the precoding matrices at the receiver. Hybrid beamforming designs use two approaches, namely fixed subarray and dynamic subarray layouts, to further simplify the hardware and consume less power. In order to enhance the EE's performance in a CRAN, it is recommended to use a K-means-based antenna design approach for the dynamic subarray architecture [90].
## 3 Results and Discussion
### Surface Morphology and Elemental Analysis Using SEM-EDS
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \multirow{2}{*}{**Element**} & \multicolumn{2}{c}{**F1**} & \multicolumn{2}{c}{**F2**} & \multicolumn{2}{c}{**F3**} & \multicolumn{2}{c}{**F4**} & \multicolumn{2}{c}{**F5**} & \multicolumn{2}{c}{**F6**} \\ \cline{2-13}  & **W \%** & **A \%** & **W \%** & **A \%** & **W \%** & **A \%** & **W \%** & **A \%** & **W \%** & **A \%** & **W \%** & **A \%** \\ \hline Carbon & \(64.5\pm 2.5\) & \(76.7\pm 1.9\) & \(67.8\pm 3.2\) & \(77.4\pm 2.5\) & \(79.4\pm 3.5\) & \(84.5\pm 2.8\) & \(65.4\pm 4.0\) & \(76.3\pm 3.2\) & \(68.7\pm 3.2\) & \(80.0\pm 2.5\) & \(39.5\pm 4.5\) & \(55.7\pm 3.6\) \\ Oxygen & \(14.9\pm 1.3\) & \(13.3\pm 1.1\) & \(19.3\pm 1.7\) & \(16.5\pm 1.5\) & \(17.7\pm 2.5\) & \(14.1\pm 2.1\) & \(18.6\pm 2.7\) & \(16.2\pm 2.4\) & \(13.6\pm 1.4\) & \(11.9\pm 1.2\) & \(24.2\pm 2.0\) & \(25.6\pm 1.8\) \\ Sodium & \(7.9\pm 0.4\) & \(4.9\pm 0.3\) & \(5.1\pm 0.7\) & \(3.0\pm 0.5\) & \(1.4\pm 0.5\) & \(0.8\pm 0.4\) & \(5.7\pm 0.6\) & \(3.4\pm 0.4\) & \(5.9\pm 0.7\) & \(3.6\pm 0.6\) & \(10.2\pm 0.7\) & \(7.5\pm 0.6\) \\ Chlorine & \(12.7\pm 0.8\) & \(51\pm 0.5\) & \(7.8\pm 0.8\) & \(3.0\pm 0.5\) & \(1.5\pm 0.5\) & \(0.6\pm 0.3\) & \(8.7\pm 0.7\) & \(3.4\pm 0.4\) & \(10.7\pm 0.9\) & \(4.2\pm 0.6\) & \(15.3\pm 0.9\) & \(7.3\pm 0.6\) \\ Silicon & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & 1.1 & 0.5 & n.d. & n.d. & n.d. & n.d. \\ Chlorine & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & \(2.9\pm 0.4\) & \(1.6\pm 0.3\) \\ Chlorine & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & 0.5 & 0.2 & n.d. & n.d. & n.d. & n.d. \\ Chlorine & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & n.d. & 1.1 \(\pm 0.2\) & \(0.3\pm 0.1\) & \(7.8\pm 0.5\) & \(2.4\pm 0.3\) \\ \hline \end{tabular} n.d. = not detected.  
\end{table} 
Table 1: Average weight concentrations (W %) and atomic concentrations (A %) of the elements detected on the surface of the MPs recovered from seawater. The color of each element is the same as the color of the corresponding symbol in Figure 10.
## 4 Results and Discussion
The results in Table 7 indicate: as the texture number increases (complex textures), the number of FP in Method 1 increases significantly, while the number of FPs in Method 2 remains relatively stable. The two methods have the same number of TP and FN and are not affected by texture changes, which shows that these two indicators are only related to the detector. Meanwhile, the evaluation indicators of the two methods can be obtained, as shown in Figure 7. It is evident from Figure 7a that the accuracy, F1-score and accuracy of Method 2 are significantly higher than those of Method 1. In addition, the three indicators of Method 2 are not affected by texture changes and have stable results. The average accuracy rate on the three different textures has reached 98.19%; since the recall rate is only related to the detector's performance, the recall rate indicators of the two methods are consistent. Furthermore, a comparative experiment was carried out on time spent by the comparator, detector, and discriminator to run a single image on the test set. The results after five rounds of testing are shown in Figure 7b. The discriminator takes the least time in the whole algorithm, and the detector takes the most time. It takes 230.29 ms to complete an image detection, of which the detection takes 165.94 ms, and the comparator and discriminator take a total of 64.35 ms, which is equivalent to 38.8% of the detector time. The extra time will not affect the production capacity of the CT production line. On the contrary, this method can significantly improve the accuracy of ceramic tile defect detection. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**SN** & **Method** & **SN** & **Defective** & **Non-Defective** & **TP** & **FN** & **FP** & **TN** \\ \hline \multirow{4}{*}{1} & & A & 170 & 170 & 166 & 4 & 30 & 140 \\  & & B & 167 & 167 & 160 & 7 & 37 & 130 \\  & & C & 187 & 187 & 181 & 6 & 51 & 136 \\  & & A & 170 & 170 & 166 & 4 & 0 & 170 \\
2 & \multirow{2}{*}{This paper} & B & 167 & 167 & 160 & 7 & 1 & 166 \\  & & C & 187 & 187 & 181 & 6 & 1 & 186 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The results from two methods. In addition, the CNFA was compared with the algorithms of previous researchers, and the results are shown in Table 8. The following conclusion can be outlined: the more complex the background texture of the CTs, the lower the accuracy of the detection result. After employing the discriminator to recheck the detection results, the accuracy of the detection results is higher than that without the discriminator, regardless of whether the background texture is complex or straightforward. Among the methods that double-check the detection results with a discriminator, our CNFA achieves the best results in complex background texture, the number of defect types, and defect detection accuracy.
## 5 Conclusions  
This study relied on a considerable number of steel- and concrete-driven pile data collected from different countries, such as Iran, Mexico, and India. The comparison of the results' assessment between the different proposed models revealed the superiority of the _DNN_ model proposed in our study, which yielded the highest accuracy in terms of _MAE_, _RMSE_, _IOS_, \(R\), _R_\({}^{2}\), and _IOA_ in both the training/validation phases. The findings indicate that this model has a high correlation coefficient, ranging between 0.9777 and 0.9998 for the validation data in the 5 splits of the \(k\)-fold cross-validation approach, meaning that there was no over-fitting or under-fitting. Furthermore, the results indicated that the aforementioned _DNN_ model is more effective compared to other empirical models proposed in the literature. The sensitivity analysis results proved that pile area had the most significant effect on the prediction of the pile-bearing capacity. Pile lengths had a moderate influence and were ranked second. In addition, cohesion and friction angle had little effect on the pile-bearing capacity. Finally, the proposed optimal model was then used to develop a _GUI_ public interface with Matlab software, named \"BeaCa2021\". The fundamental benefit of \"BeaCa2021\" is to help researchers and practicing civil engineers, regardless of their proficiency, interested in the problem of modeling, to estimate the pile-bearing capacity with the benefits of gaining time and money.  
This work has opened up several questions that need further investigations to overcome certain limitations. Firstly, there is a need to use more data from other countries to enhance the learning phase, which is needed to develop the BeaCa2021 in the future. Secondly, we propose the usage of meta-heuristic algorithms combined with machine-learning methods for predicting the pile-bearing capacity in future studies. These algorithms have demonstrated high-performance results when used with machine-learning techniques, leading to improved learning.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app112210908/s1](https://www.mdpi.com/article/10.3390/app112210908/s1)]([https://www.mdpi.com/article/10.3390/app112210908/s1](https://www.mdpi.com/article/10.3390/app112210908/s1)), Table S1: The Database about Two Types of Materials.  
Conceptualization, M.A.B.; methodology, M.A.B. and H.Z.; software, M.A.B. and L.G.; validation, M.A.B. and A.-I.P.; formal analysis, M.A.B. and L.L.; investigation, M.A.B.; resources, M.A.B.; data curation, M.A.B.; writing--original draft preparation, M.A.B., L.L., and A.-I.P.; writing--review and editing, M.A.B., L.L., and A.-I.P.; visualization, M.A.B. and A.-I.P.; supervision, M.A.B., H.Z., and A.-I.P.; project administration, M.A.B. and L.G.; funding acquisition, M.A.B. and A.-I.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Materials and Methods  
All materials and methods are described in greater detail by [PERSON]'s (2017) [34] graduate thesis. Below is a summary of key details.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Location**} & \multicolumn{2}{c}{**Measured Concentration (ug-m\({}^{-3}\))**} & \multirow{2}{*}{**\% Difference (TWA SPME vs. Sorbent Tubes)**} & \multirow{2}{*}{\(p\)**-Value**} \\  & **TWA SPME (Glass Liner)** & & & \\ \hline Office & 17.7 (\(\pm\)2.7) & 9.7 (\(\pm\)1.0) & 58 & 0.002 \\ Laboratory & 15.2 (\(\pm\)0.8) & 6.6 (\(\pm\)0.7) & 78 & 0.0001 \\ Farm 1, Day 1 & 3620 (\(\pm\)430) & 755 (\(\pm\)20) & 131 & 0.0004 \\ Farm 1, Day 2 & 2400 (\(\pm\)310) & 750 (\(\pm\)180) & 104 & 0.0008 \\ Farm 2, Day 1 & 685 (\(\pm\)270) & 340 & 67 & 0.002 \\ Farm 2, Day 2 & 750 (\(\pm\)90) & 375 (\(\pm\)20) & 67 & 0.0001 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of measured acetic acid concentrations in different locations using time-weighted average (TWA) solid-phase microextraction (SPME) (85 \(\upmu\) Car/PDMS) facilitated with a glass liner and sorbent-tube-based measurement.
## 2 Materials and Methods  
### Determination of an Optimal Inspection Calendar  
Consider an equipment unit scheduled for overhauling within a certain period, where an evident critical failure mode exists for which a P-F time interval is known and monitored off-line over time. Contrary to the case of PM, where a component is regularly repaired or replaced, in PdM, the interval between inspections might not be constant. Moreappropriately, it will vary with the course of the failure mechanism. When, for instance, the hazard rate \(h(t)\) increases with time, the adoption of shorter and shorter time intervals seems to make common sense. A calendar may thus assume a minimum threshold of reliability between inspections. If optimization is envisaged, then all costs have to be taken into consideration [2,18]. According to [PERSON] [6], the reliability between inspections is a conditional probability; that is, the probability \(R\) that a failure will not happen within a period \(\Delta t\) (time until next inspection) is given by \(R(\Delta t\,|\,t)=\frac{R(t+\Delta t)}{R(t)}\). From this expression, one can determine \(R(t+\Delta t)=R(\Delta t\,|\,t)\cdot R(t)\). Because reliability must be kept constant between successive inspections, the condition \(R(\Delta t\,|\,t)=R(t)=R_{i}\) must apply. Therefore, considering \(n\) as the \(n\)th inspection, one can determine (1):  
\[R(t+\Delta t)=R^{n} \tag{1}\]  
This expression can now be combined with any distribution that might describe the specific failure behavior. If it is combined with the Weibull distribution, the time intervals between every two inspections are determined by the expression [6,19]:  
\[t_{n}=t_{0}+\beta\cdot\left[-\ln(R^{n})\right]^{\frac{1}{\alpha}} \tag{2}\]  
Keeping \(R(t)\) constant between successive inspections and giving \(\alpha\) the values described below, one can conclude  
* when \(\alpha<1\), \((t_{n+1}-t_{n})<(t_{n+2}-t_{n+1})\), and the risk function \(h(t)\) decreases;
* when \(\alpha=1\), \((t_{n+1}-t_{n})=(t_{n+2}-t_{n+1})\), and the risk function \(h(t)\) is constant; and
* when \(\alpha>1\), \((t_{n+1}-t_{n})>(t_{n+2}-t_{n+1})\), and the risk function \(h(t)\) increases.  
As the risk function decreases, the time interval between inspections increases and vice-versa. The value \(\alpha=1\) plays the role of a \"division line\" between infant and wear-out failures. The model described so far has not considered the existence of a P-F time interval (see Figure 1). According to [PERSON] [7], when a failure commences it deteriorates to the point at which it can be detected (point P). If it happens not to be detected and corrected, the deterioration process proceeds--usually at an increasing rate--until the point of functional failure (point F) is reached. Sometimes an intermediate point M is defined as the minimum time interval (M-F) available for an action to be carried out to prevent functional failure. If expression (2) is merged with the P-F time interval and all related costs, a mathematically optimized calendar of inspections can be obtained.
# Fire Resistance of Ultra-High-Strength Steel Columns Using Different Heating Rates
## 2 Methodology and Validation  
In this section, the numerical model used to simulate the UHSS columns at both room and fire temperatures will be presented. The modelling was performed using Ansys Mechanical APDL 2023 R2, and the results are compared with the numerical and experimental results obtained by [PERSON] et al. [11; 12].  
The UHSS nominal yield strength is 1200 MPa, and it was manufactured by SSAB steel company. The UHSS material owes its ultra-high strength to its martensitic microstructure, which is obtained by direct quenching. The manufacturing process also considers a longitudinal high-frequency induction welding technique. The chemical composition was supplied by the manufacturer, see Table 1.  
The austenitic stainless steel grade 1.4301 was also used in combination with UHSS to produce the hybrid columns.  
For the simulations in Ansys, different types of analyses were carried out to comprehensively understand the behaviour of the columns under different conditions. Initially, an elastic linear buckling analysis was conducted at room temperature to establish a baseline for deformation, crucial for incorporating geometric imperfections in subsequent nonlinear analyses. This step was followed by a nonlinear buckling analysis at room temperature to determine the load-bearing capacity and validation at ambient temperature [13; 14].  
As the focus shifted to elevated temperatures, another nonlinear buckling analysis was developed to validate the column's performance when subjected to high temperatures, specifically targeting the surface temperature of the column. For scenarios involving  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline
**Element** & **C** & **Si** & **Mn** & **P** & **S** & **Cr** & **Ni** & **Mo** & **B** \\ \hline Content (\%) & 0.230 & 0.800 & 1.700 & 0.025 & 0.015 & 1.500 & 1.000 & 0.500 & 0.005 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical elements of the UHSS CHS [11].  
standard furnace temperatures, an additional nonlinear thermal analysis was performed to map out the temperature distribution across the columns due to the furnace gas temperature (different heating rate). Finally, a thermo-mechanical analysis was carried out to validate the columns' behaviour under high-temperature conditions, with superposition of the nodal temperatures over time, keeping the mechanical load constant.  
Given that these simulations deal with long columns featuring reduced thicknesses due to the utilisation of UHSS, shell finite elements were used. The Shell 181 element was used, featuring a four-node 3D element with six degrees of freedom at each node (translation in the x, y, z directions and rotation around the x, y, z axes). This element uses linear interpolating functions, with full integration on the plane of the element (2 \(\times\) 2) and 5 Gauss integration points over thickness. In thermal simulations, the Shell 131 element, also a four-node 3D element, was employed, offering up to 32 degrees of freedom at each node, related to temperature that depends on the number of layers defined by the user [14].
## 4 Security Attacks
### Network Layer
#### 4.2.6 DNS Attack  
DNS attacks [18] target the domain name system (DNS) to compromise blockchain nodes, particularly new ones. In this scenario, an adversary manipulates the DNS cache by injecting false node information. As a result, a new node, during its bootstrapping process, may connect to a malicious set of peers instead of legitimate nodes. This false neighborhood can be exploited to manipulate transactions or the consensus process. Common defenses against DNS attacks include secure bootstrapping mechanisms, periodic DNS cache flushing, and robust peer verification protocols to ensure that connections are only established with trusted nodes.
## 2 Materials and Methods  
### Titanium Alloys  
In this work, the femoral implant is designed and manufacturing is investigated using Ti-6 Al-4V. Ti-6 Al-4V is one of the most used materials in the manufacture of implants. Its low density, high hardness, high corrosion resistance and excellent biocompatibility make it a real option for biological applications. When comparing Ti with other biocompatible metals such as stainless steel and the Co-Cr alloy, we found that titanium has a much lower density, being 4.7 g/cm; that of stainless steel is 7.9 g/cm and that of the CoCrMo and CoNiCrMo alloys is 8.3 g/cm and 9.2 g/cm, respectively. Titanium exhibits mechanical properties similar to those of these metals and a higher resistance to corrosion. This Ti metal and its alloys are very difficult to work cold, so it is usually forged hot, at a temperature close to 800 \({}^{\circ}\)C.  
Both titanium and its alloys have a Young's modulus of around 110 GPa. This value is practically half that of other metals such as stainless steel or the Co-Cr alloy, which is why titanium is the metal most elastically compatible with human bone. Regarding the mechanical properties of this metal, it is observed that both the elastic limit and the tensile strength are above that of stainless steel and Co-Cr alloy. Even though some authors provide warnings regarding the biocompatibility of vanadium in the literature, there is no study that shows that this element can be harmful when it is combined in the Ti-6 Al-4V alloy.  
Ti-6 Al-7 Nb alloy emerged as a commercially available alternative to Ti-6 Al-4V due to concerns about the potential carcinogenicity of vanadium. As Ti-6 Al-7 Nb exhibits mechanical properties similar to Ti-6 Al-4V [23], its biocompatibility and stress distribution characteristics should not differ in the proposed design. However, it is essential to conduct dedicated simulations to accurately assess the performance of Ti-6 Al-7 Nb implants with specific identified properties and assess the model constants of the raw material.
# Coordination of Macro Base Stations for 5G Network with User Clustering
## 3 Problem Reformulation with User Clustering and Benders Decomposition  
Compared with 4G, the number of 5G terminal devices has grown explosively. If we consider the optimal allocation of each user in (\(\mathbf{P}_{1}^{t}\)), the optimization problem will be too complex to be solved. In addition, (\(\mathbf{P}_{2}\)) is a large-scale, mixed-integer linear programming problem (MILP) and is difficult to solve directly when the number of considered BSs grows. To solve the above problems, a user clustering method for (\(\mathbf{P}_{1}^{t}\)) and the Benders decomposition algorithm for (\(\mathbf{P}_{2}\)) are adopted, respectively, to accelerate the solution process.
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 6 Proposed Algorithm: Communication Optimisation Integration
### Algorithm Evaluation for LB = 106 dB and LB = 115 dB  
Further, we evaluate the algorithm when one of the technical solutions in [57] (long range, \(LB=106\) dB) and [59] (LoRa/LoRaWAN, \(LB=115\) dB) is used. The same distances, \(d=2.393\) km, \(d=7.2209\) km and \(d=7.2209\) km, were investigated.  
.2.1 Algorithm Evaluation for \(LB=106\) dB and \(LB=115\) dB when \(d=2.393\) km or \(d=7.2209\) km  
For evaluating the technical solutions in [57; 59], we considered the simulation parameters in Table 13. The values are identical with the ones used for the technical solution in [58] (for comparison, see Sections 7.1.1 and 7.1.2, Tables 10 and 11). The technical solution based on long range communication [57] succeeds in assuring the communication link without any improvements from the point of view of UV placement (without it being necessary to apply the algorithm and to move the UVs from their initial positions). The same is valid for the solution in [59], both being candidates to ensure the communication link between GW and any of the UVs while transmitting their GPS coordinates.