esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Analysis
### Bit-Error-Rate Computation
So the overall error probability of NU is only affected by the erroneous estimation of antenna index and modulation symbol in S\({}_{1}\) and S\({}_{2}\), respectively. * The desired information of FU is the opposite of NU in \(P_{1}\), whereas the desired information of FU in \(P_{2}\) is the transmitted modulation symbol by D\({}_{1}\) irrespective of S\({}_{1}\) and S\({}_{2}\). Therefore, the overall error probability of FU as shown in (5) and (6) is only affected by erroneous estimation of modulation symbol and antenna index in S\({}_{1}\) and S\({}_{2}\), respectively during \(P_{1}\) as well as erroneous estimation of modulation symbol during \(P_{2}\). * Lastly, the overall error probability of D2D user D\({}_{2}\) is only affected by the erroneous estimation of antenna index in \(P_{2}\) only. Recall that BS and D\({}_{1}\) transmit by activating only one antenna due to the exploitation of SM. Hence, as a special case, any transmitted \(M\)-PSK/\(M\)-QAM symbol estimation at D\({}_{1}\) and FU in S\({}_{1}\) can be considered as \(1\times A_{\text{D}_{1}}\) and \(1\times A_{\text{FU}}\) MRC detection, respectively [12]. Similarly, any transmitted \(M\)-PSK/\(M\)-QAM symbol estimation at NU in S\({}_{2}\) can be considered as \(1\times A_{\text{NU}}\) MRC detection. The analytical formulation of error probabilities of FU and NU under this special case is provided in Appendix A. Moreover, the overall error probability of the corresponding user for erroneous estimation of antenna number can be calculated by [12] (Equation (10)).
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 5 Signal Technologies
### Infrared-Based Systems (IR)  
Infrared (IR) radiation is electromagnetic radiation, the wavelength between 700 nm and 1 mm is greater than that of the visible spectrum but less than that of radio waves [55]. Its main advantage is its wide availability since many devices are equipped with IR sources. However, as they require line-of-sight communication and fail to penetrate opaque obstacles such as walls, their use is limited in individual rooms. It is a very reliable system as light cannot pass through the walls; hence, it is impossible for a tag to detect light from an anchor without being in the same room. It is, however, subject to interference from other sources of IR devices or possibly blinded by direct sunlight. For precise localization, many anchors are required, and we can have difficulties due to the low quality of the signal strength measurements required to calculate the position. Mobile node localization can be used as a measurement method for estimating the angle of arrival of the IR signals [56]. These systems can be divided into two types: direct infrared systems and diffuse infrared systems. The former uses a point-to-point data transmission standard achieving low-power communication. It requires line of sight (LOS) communication or a very short distance between the devices. The latter has a stronger signal than direct IR; it has a more extended reach (9-12 m). It uses wide-angle LEDs, which emit signals in many directions [57]. Another system tested is based on a single photodiode (PD) and multiple LEDs; it uses the angular diversity transmitter, which consists of multiple LEDs and a biconvex lens [58]. Furthermore, systems based on mixed IR and ultrasound [59] or acoustic signatures have been proposed [60].
Complexity has been reported regarding the virulence of NTS, as several genes involved in invasion and adhesion such as _agfA_, _lpfA_, _hiA_, _sivH_ and _invA_ have been detected [8]. The virulence involves an extensive set of factors, which are mainly encoded in virulence genes within _Salmonella_ pathogenicity islands (SPIs) [9]. Twenty-four SPIs have been identified and are either located on the chromosomes or on plasmids, though not all virulence genes are embedded in these pathogenicity islands [9; 10]. Furthermore, SPIs are capable of being horizontally transferred to other enteric bacteria, which may cause non-pathogenic microorganisms to become pathogenic [11]. Encoded virulence genes have various roles in pathogenesis, such as facilitating the attachment, invasion and intracellular survival of _Salmonella_ and bypassing the hosts' defense mechanisms, resulting in diverse clinical symptoms based on the virulence genes present [9]. Globally, the extensive use of antimicrobial agents in animal production and clinical practices have contributed to the increasing pervasion of resistance of _Salmonella_ strains toward antimicrobials, and thus the emergence of multi-drug resistant (MDR) strains [12]. Outbreaks caused by MDR- NTS have been linked to higher rates of hospitalization and treatment failures of patients and thus higher morbidity and mortality rates over the years [13; 14]. In South Africa, the hygiene, quality of meat and number of units that can be slaugtered are primarily regulated via the Meat Safety Act, 2000 (Act No. 40 of 2000) and the Foodstuffs, Cosmtics and Disinfectants Act, 1972 (Act No. 54 of 1972). The KwaZulu-Natal province was selected as it is the second most important cattle producer in South Africa and a significant contributor to the supply of beef in the country [15]. With the predominant source of _Salmonella_ infection being animal-originating foods [14] and South Africa having an ever-expanding beef industry [16], the raw beef production chain, from farm to abattoirs and retail outlets should be regularly investigated. Detailed knowledge of the virulence factors of _Salmonella_ serovars from different beef products in KZN retail is limited [17]. The aim of this study was to expand the knowledge on the prevalence, serovars, antimicrobial resistance and virulence factors from _Salmonella_ isolates that were recovered from beef and beef products among selected retail outlets from the KwaZulu-Natal province in South Africa. The samples represented various degrees of processing, including raw intact beef, raw beef organs, raw processed beef and ready to eat beef products such as biltong and cold meats. The information provides knowledge about the pathogenicity of _Salmonella_ species that circulate in the food value chain and could pose a potential health threat to consumers.
## 5 Simulation Results
We set the rate of the code \(R=4\), \(\epsilon=0.15\), and the length of information as 128. It can be seen from Figure 11 that the BLER of [PERSON] remains 0 and the BLER of [PERSON] increases with \(\theta_{e}\). When \(M=8\), the BLER of [PERSON] reaches 1 for \(\theta_{e}=50^{\circ}\), which means that eavesdroppers beyond \(5^{\circ}\) cannot decode the information. When the number of antennas reaches 32, eavesdroppers beyond \(1^{\circ}\) also cannot decode the message. In the case where the location of [PERSON] is unknown to [PERSON], [PERSON] can design beamforming schemes using the location of [PERSON] that results in a sufficiently low BLER for [PERSON]. In this way, the chosen location of [PERSON] and more distant areas are protected. For the region between Bob and the chosen location of [PERSON], additional surveillance measures can be employed to prevent eavesdropping.
# Further Development of 3D Crack Growth Simulation Program to Include Contact Loading Situations
## 5 Discussion of Results and Future Work  
In order to incorporate different loading situations in structures for crack growth simulations, the functionality of the numerical crack growth simulation program Adapcrack3D is extended with the option to simulate models in consideration of contact definitions. For this purpose, the program is updated to create models with multiple part definitions, contact definitions, etc. This has been successfully tested with the model of clinched joints. In addition, the possibility of simulating models with contact definitions between the crack surfaces is also analyzed. A new geometry of the submodel is proposed for crack growth analysis for any arbitrary crack with contact definitions. The method has been tested with different 3D models, and the results are in good agreement with the reference models. In addition, simulations are performed to compare the results from Adapcrack3D with experimental [43] and numerical [44] results from the literature. The comparison of the results shows that they are in good agreement with the results from the literature. There are various application areas in which this newly implemented feature in Adapcrack3D can be applied, for instance rolling contact fatigue in rails, fatigue crack growth in gears, bearings, etc. [44; 45; 46; 47; 48]. As future work, the crack growth simulation in clinched joints has to be performed based on the experimental results from [PERSON] et al. [49]. The authors analyzed the crack initiation location in clinched joints under cyclic loading conditions. As described in the work, these cyclic loading situations created three failure modes, which were experimentally obtained and are verified with the help of numerical simulations. As  
Figure 17: Stress intensity factors for Mode I and Mode II from the literature and Adapcrack3D: (**a**) K\({}_{\text{I}}\) along the crack front in which the load starts before the crack and rolls over it (according to [44]); (**b**) K\({}_{\text{II}}\) along the crack front in which the load starts before the crack and rolls over it (according to [44]); (**c**) K\({}_{\text{I}}\) calculated by Adpcrack3D along the crack front when the wheel rolls over the rail; (**d**) K\({}_{\text{II}}\) calculated by Adpcrack3D along the crack front when the wheel rolls over the rail.  
the crack initiation location and loading conditions are known, Adapcrack3D can now be utilized to perform crack growth simulations in the clinched joints for all the three crack initiation locations.  
Writing--review & editing, T.D.J., D.W., B.S. and G.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)--TRR 285-Project--ID 418701707.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the big size of models and simulation data.  
The authors declare no conflict of interest.
## 3 Preliminaries
### Energy Consumption Model  
The energy consumption model in this paper is the same as the radio model in Ref. [23]. In this model, the transmitter dissipates energy to run the radio electronics and the power amplifier. The receiver dissipates energy to run the radio electronics.  
The radio electronics and power amplifier are responsible to send data. The energy consumption includes two modes: (1) free space mode and (2) multipath fading mode. If the data transmission distance \(d\) is less than the threshold distance \(d_{0}\), free space mode is used. Otherwise multipath fading mode is used. Let the Signal to Noise Ratio (SNR) be reasonable in the circuit, the energy consumption that a sensor node sends \(l\)-bit data packet is given by the following equations.  
\[E_{Tx}\left(l,d\right)=\begin{cases}l\times E_{elec}+l\times\varepsilon_{fs} \times d^{2},d\leq d_{0}\\ l\times E_{elec}+l\times\varepsilon_{mp}\times d^{4},d>d_{0}\end{cases} \tag{3}\]  
where \(E_{elec}\) is the energy dissipated per bit to run the transmitter or the receiver circuit. \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) are the power amplification energy consumption coefficients in different energy consumption modes. \(d_{0}\) is the threshold transmission distance, and \(d_{0}\) is given by  
\[d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}} \tag{4}\]  
Figure 2: Directional sensor model.  
To receive \(l\)-bit data, the energy consumed by the receiver is given by  
\[E_{Rx}\left(l\right)=l\times E_{dec} \tag{5}\]  
In general, the communication energy parameters can be set as: \(E_{Tx}=50\) nJ/bit, \(\varepsilon_{fs}=10\) pJ/bit/m\({}^{2}\), \(\varepsilon_{mp}=0.0013\) pJ/bit/m\({}^{4}\), \(d_{0}=87\) m.
# Short-Term Deformability of Three-Dimensional Printable EVA-Modified Cementitious Mortars
## 3 Materials and Methods
### Method
#### 3.2.2 Preparation of Specimens  
Three test specimens were produced for each EVA/cement ratio (i.e., test variable). Among each set of three test results produced for each EVA/cement ratio, the middle value was selected because all of the results appeared continuously. Casting the specimens with mortar directly extruded into the formwork created many voids, resulting in significant data errors. Hence, ASTM C109/C109M-02: Standard Test Method for Compressive Strength of Hydraulic Cement Mortars [16] was applied to produce the specimens. These specimens were cured at a temperature of \(23\pm 2\)\({}^{\circ}\)C and a relative humidity of \(65\%\pm 5\%\). Also, the cylindrical specimens used for the compressive stress-strain tests were ground for plaeness before the tests were conducted.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Appearance** & **Bulk Density (kg/m\({}^{3}\))** & **Moisture Content (\%)** & **Particle Size (0.074 mm, \%)** \\ \hline White powder & 430 & & \(\leq\)12 & & \(\geq\)95 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Properties of viscosity modifying agent.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**EVA/Cement** & **W/C** & **EVA** & **Cement** & **Water** & **Silica** & **Fly** & **Silica** & **Super-** & **Viscosity** \\
**Ratio** & **Ratio** & **EVA** & **Cement** & **Water** & **Sand** & **Ash** & **Fume** & **Plasticizer** & **Modifying Agent** \\ \hline
0 & 0.45 & 0 & 642 & 289 & 1377 & 184 & 92 & 6 & 0.3 \\
0.05 & 0.46 & 32 & 638 & 294 & 1368 & 182 & 91 & 6 & 0.3 \\
0.10 & 0.51 & 63 & 635 & 324 & 1360 &
## 5 Models Development
### PSO Parameters  
Parameters such as \(C_{1}\), \(C_{2}\), \(w\), and the number of particles (population) should be also determined in the PSO algorithm. For this purpose, the ANN-PSO model was run several times for different parameters. It was seen that the fastest convergence rate and the best cost values for the PSO algorithm  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Neurons**} & \multicolumn{3}{c}{**Train**} & \multicolumn{3}{c}{**Test**} \\ \cline{2-7}  & \(r\) & \(R^{2}\) & \(RMSE\) (kN) & \(r\) & \(R^{2}\) & \(RMSE\) (kN) \\ \hline
5 & 0.917 & 0.841 & 22.049 & 0.911 & 0.829 & 22.791 \\
8 & 0.944 & 0.891 & 18.221 & 0.940 & 0.883 & 18.896 \\
10 & 0.950 & 0.903 & 17.215 & 0.958 & 0.904 & 17.488 \\
15 & 0.947 & 0.897 & 17.583 & 0.950 & 0.902 & 17.464 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the ANN model for different architectures (output = load). RMSE: root mean squared error.  
Figure 6: ANN architecture.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Neurons**} & \multicolumn{3}{c}{**Train**} & \multicolumn{3}{c}{**Test**} \\ \cline{2-7}  & \(r\) & \(R^{2}\) & \(RMSE\) (mm) & \(r\) & \(R^{2}\) & \(RMSE\) (mm) \\ \hline
5 & 0.858 & 0.737 & 2.077 & 0.824 & 0.680 & 2.359 \\
8 & 0.874 & 0.764 & 1.983 & 0.821 & 0.675 & 2.335 \\
10 & 0.884 & 0.781 & 1.913 & 0.831 & 0.687 & 2.394 \\
15 & 0.882 & 0.778 & 1.915 & 0.827 & 0.685 & 2.382 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of the ANN model for different architectures (output = slip).  
could be obtained when \(C_{1}\) and \(C_{2}\) are equal to 1 and 2, respectively. In addition, an initial value of \(w=1\) with a damping coefficient of 0.99 has been used. To determine the number of particles (swarm size), the ANN-PSO model was rerun for different swarm sizes. A summary of the obtained values of \(RMSE\), \(R^{2}\), and \(r\) in the different number of particles can be seen in Tables 6 and 7.  
Figure 7 demonstrates the impact of the number of particles on the root mean squared error (\(RMSE\)). As can be observed in these diagrams, the lowest value of \(RMSE\) is obtained when the number of 45 particles is selected. More importantly, in this number of particles, the lowest difference between the \(RMSE\) values in the training and testing phases has been obtained. This implies that in this number of particles, overfitting is less likely to occur. Considering these facts, the number of 45 particles has been adopted in the PSO algorithm.
# Synthesis and Biological Studies of New 2-Benzoxazolinone Derivatives as Antibacterial Agents
## 2 Materials and Methods
### General Procedure for the Preparation of Hydrazones 8-23
#### 2.4.4 Agar Diffusion Methods  
The agar well diffusion and disc diffusion methods were used to compare the antibacterial activity of compounds **4**, **7**, **12**, **22**, and **23** against bacteria strains.  
The selected strains of bacteria were used to determine the bacterial properties and the bacterial inocula were spread over an agar plate for both methods. The bacterial inoculum was adjusted to 0.5 MacFarland, and a volume of 0.1 mL containing 1.5 \(\times\) 10\({}^{7}\) bacteria was used on the entire surface of a Mueller-Hinton agar (MHA) Petri plate with a sterile cotton-tipped swab to form an even lawn.
###### Abstract  
Cement is widely used in marine concrete, and its resistance to chloride ion corrosion has been widely considered. In this paper, based on a laboratory test, the influence of different hydrostatic pressures, coarse aggregate contents and w/c ratios on the chloride resistance performance is analyzed. Based on COMSOL finite element software, a two-dimensional cementitious materials model is established, and the simulation results are compared with the experimental results. The results show that the penetration depth of chloride ions in cement increases with the increase of the w/c ratio. Under the hydrostatic pressure of 0 MPa, when the w/c ratio is 0.35, the penetration depth of chloride ions is 7.4 mm, and the simulation result is 8.0 mm. When the w/c ratio is 0.45, the penetration depth of chloride ions is 9.3 mm, and the simulation result is 9.9 mm. When the w/c ratio is 0.55, the penetration depth of chloride ions is 12.9 mm, and the simulation result is 12.1 mm. Under different hydrostatic pressures, the penetration depth of chloride ions obviously changes, and with the increase in hydrostatic pressure, the penetration depth of chloride ions deepens. Under the w/c ratio of 0.35, when the hydrostatic pressure is 0.5 MPa, the penetration depth of chloride ions is 11.3 mm, and the simulation result is 12.1 mm. When the hydrostatic pressure is 1.0 MPa, the penetration depth of chloride ions is 16.2 mm, and the simulation result is 17.5 mm.  
concrete; chloride transport; hydrostatic pressure; aggregate content; w/c ratio; COMSOL software +
Footnote †: journal: Applied Sciences
## 3 Operational Requirements
### Technical Specifications
#### 3.4.9 Environmental Conditions  
The ability to operate in a wide range of temperatures is critical for fruit-harvesting robots, as robots must operate in both hot and cold climates. The robot's electronic and mechanical components are designed to operate effectively and safely within a specific range of temperatures. This ensures that the robot can perform its fruit-harvesting duties without being affected by extreme environmental conditions [78].  
The materials used in the manufacture of the robot must be able to withstand extreme temperatures without significant degradation. For example, engineering plastics and metal alloys are used to maintain their structural and electrical properties in extreme heat or cold conditions. In warm environments, active cooling systems, such as fans or heat sinks, can be integrated to keep component operating temperatures within safe limits. This prevents overheating and ensures the robot's operational reliability [12].  
Water resistance is crucial for fruit-harvesting robots, as these often operate in fields where humidity can be high due to irrigation or variable weather conditions. Protection against water intrusion ensures that the robot's sensitive electronics and mechanical systems can operate without risk of moisture damage. Harvesting robots usually comply with IP (Ingress Protection) standards, which indicate the level of protection against solids and liquids. For example, an IP67-rated robot can be temporarily submerged in water to a certain depth without damage. Robots can be equipped with environmental sensors that continuously monitor the temperature and relative humidity conditions of the environment. This data can be used to dynamically adjust the robot's operation and optimize its performance under varying environmental conditions. Adaptive control algorithms allow the robot to automatically adjust its operating parameters according to the detected environmental conditions, ensuring consistent and efficient performance in different environments.  
These specifications ensure that the fruit-harvesting robot can operate efficiently, safely, and autonomously in various agricultural environments, optimizing harvesting and minimizing damage to fruits and plants.
## 2 Evolution of Maritime Communication Networks
### Security and Trust Considerations in 6G-Enabled MCNs  
The convergence of terrestrial and non-terrestrial infrastructure in 6G-based MCNs introduces new challenges related to security vulnerabilities stemming from the distributed nature of nodes (ships, UAVs, satellites), constrained onboard resources, and mission-critical operations at sea. These vulnerabilities range from eavesdropping on satellite links, spoofing of AIS signals, and cyber-physical attacks on autonomous ships to compromised relay nodes in UAV-aided networks [46].  
To address these challenges, several 6G-enabling technologies can be leveraged. First, Post-Quantum Cryptography (PQC) has been recently studied and can be used to ensure resilience against quantum-based threats, enabling long-term security for inter-platform communication [46]. Second, blockchain and distributed ledger technologies can provide decentralized identity management and data integrity, suitable for multi-stakeholder environments like international shipping or port operations [46]. Third, AI-powered anomaly detection systems, including both centralized and federated schemes, can monitor traffic, behavioral patterns, and device health, providing early detection of intrusions or malfunctions across dynamic maritime topologies. Other security frameworks, like Physical Layer Security (PLS) techniques, such as beamforming with RIS, can further reduce the risk of interception by shaping the radio environment [46]. Integrating such mechanisms into 6G MCNs is essential for achieving trustworthy, resilient, and autonomous maritime operations.