esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sensing Method
### Ultrasonic Sensing
#### 2.4.2 Developing Technologies  
One of the key challenges in ultrasonic sensing lies in the accurate reconstruction of ultrasonic signals into image signals. This is particularly pronounced in the case of implantable ultrasonic sensors. As their positions relative to the human body vary over time, ultrasonic images often suffer from distortion. Machine learning algorithms have been employed to rectify the image distortion induced by the displacement of ultrasonic transducers [83]. Nevertheless, this problem has not been fully resolved, and further investigation is still warranted.  
Moreover, the disparity in elastic modulus between ultrasonic devices and human tissues represents another issue that demands attention. This mismatch can impede the smooth propagation of ultrasonic waves into human tissues. Currently, the prevalent approach is to utilize gels to form a coupling layer between the ultrasonic element and the skin. Acoustic gels and hydrogels are frequently utilized [84,85]. Nevertheless, after a certain period, these two types of gels tend to dry out and detach, rendering long-term medical detection unfeasible. Identifying a coupling material with optimal acoustic properties, long-term usability, and excellent adhesion and compatibility with both the skin and the transducer remains a significant challenge to be addressed in the field of ultrasonic sensing [86].  
The table below comprehensively summarizes the detectable parameters and their corresponding performance of various sensing mechanisms. (Table 1.)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Reference** & **Sensing Parameters** & **Sensing Performance** \\ \hline \multirow{4}{*}{Magnetic field sensor} & Miniature extravascular & \multirow{2}{*}{Mean arterial pressure (ABP)} & \multirow{2}{*}{MAE less than 5 mmHg} \\  & hall-based magnetic & & \\  & sensor [15] & & \\  & Millimeter-level magnetic & & \\  & implants [16] & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \end{tabular}
\begin{tabular}{c c c} \hline \hline \multirow{4}{*}{Magnetic field (ASP)} & \multirow{2}{*}{Mean arterial pressure (ABP)} & \multirow{2}{*}{MAE less than 5 mmHg} \\  & & sensor [15] & & \\  & Millimeter-level magnetic & & \\  & implants [16] & & \\  & & &
###### Abstract  
With their unique capability to deal with a considerable geographic area, satellite-ground-underwater optical wireless communication (OWC) systems are an appealing alternative to meet the ever-increasing demand for end-to-end broadband services. Using four different Laguerre-Gaussian (LG) modes, an orbital angular momentum (OAM) multiplexing method was developed to enhance the spectral efficiency and system capacity of the satellite-ground-underwater OWC system. At an aggregate throughput of 160 Gbps, LG[0,0], LG[0,2], LG[0,4], and LG[0,8] were realized. Various atmospheric conditions, water types, and scintillation effects were used to evaluate the performance of two separate OWC links for satellite-to-ground and ground-to-underwater communication. A maximum OWC range of 21,500-30,000 km has been obtained under weak-to-strong turbulence for satellite-to-ground scenarios, and a range of 12-27 m underwater for ground-to-underwater scenarios under various scintillation effects. At LG[0,0], in pure sea, the maximum gain is \(-\)75.02 dB, the noise figure is 75.02 dB, the output signal is \(-\)78.32 dBm, and the signal-to-noise ratio is 21.67 dB. In comparison with other works in the literature, this system shows a superior performance.  
MDM; OAM; OWC; satellite; underwater +
Footnote †: journal: _Photonics_
## Abbreviations  
Abbreviation used in the article:  
DNN (s) Destination (s) RLN (s) Relay node (s) DSN (s) Destination node (s) PDR Packet delivery ratio BER Bit error rate AF Amplify and forward DF Decode and forward  
Figure 9: Network Delay.  
RSSI Received signal strength indicator DLL Data link layer OSI Open system interconnection MAC Media access control GPS Global position system SNR Signal to noise ratio MRC Maximal ratio combine
# A Dynamic Analysis Method of Liquid-Filled Containers Considering the Fluid-Structure Interaction
## 4 Modal Analysis and Time-Historical Analysis of Cylindrical Liquid-Filled Containers  
### Liquid Modal Analysis  
For the cylindrical liquid-filled container in Figure 19, the liquid sloshing modes in elastic and rigid containers were analyzed separately. As illustrated in Figure 20, the eight-node acoustic fluid elements are used to model the water domain, and the eight-node solid elements are used to model the cylindrical container shell. The model sizes and material parameters are listed in Table 3 [8].  
Figure 19: Cylindrical liquid-filled containers. (**a**) Elastic container. (**b**) Rigid container.  
Figure 18: Three-dimensional view of liquid sloshing modes: (**a**) 1 st; (**b**) 2 rd; (**c**) 3 rd; (**d**) 4 th.  
The liquid sloshing frequency in the cylindrical liquid-filled container is [2; 3]  
\[\omega_{i}=\sqrt{\lambda_{i}\frac{\text{g}}{R}\text{tanh}h\left(\lambda_{i}\frac{h }{R}\right)} \tag{11}\]  
where \(\lambda_{i}\) is the \(i\)th root of the derivative of the family of Bessel functions; g is the gravitational acceleration; \(R\) is the radius of the liquid-filled container; and \(h\) denotes the liquid level in the liquid-filled container.  
Figure 20: Finite element model of elastic container: (**a**) 3D view. (**b**) Two-dimensional elevation through a central cutting plane.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Physical Qualities** & **Symbol** & **Value** \\ \hline Container diameter & \(D\) & 12 m \\ Container height & \(H\) & 7 m \\ Container wall thickness & \(t\) & 0.2 m \\ Liquid level & \(h\) & 6 m \\ Container elastic modulus & \(E\) & \(3.10\times 10^{10}\) Pa \\ Container density & \(\rho_{1}\) & 2643 kg m\({}^{-3}\) \\ Container Poisson’s ratio & \(\mu\) & 0.15 \\ Liquid density & \(\rho_{2}\) & 1000 kg m\({}^{-3}\) \\ Liquid acoustic velocity & \(c\) & 1435 m s\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model sizes and material parameters.  
The first five liquid sloshing frequencies in the cylindrical liquid-filled container are listed in Table 4. The liquid sloshing modes in the cylindrical liquid-filled container and the vibration modes of this container are shown in Figures 21 and 22, respectively. The FEA numerical solution conforms to the theoretical solution, which indicates that it is reliable and can be used to simulate liquid sloshing modes in containers. Moreover, the FEA predictions of liquid sloshing frequencies in rigid and elastic containers are same for this example, suggesting that the influence of structural stiffness on the liquid sloshing modes can be ignored.
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 8 Performance Analysis
### Communication Cost  
We evaluated the communication costs of our protocol with existing schemes [6; 8; 15; 16]. According to [34], we define that the identity, timestamp, and random number are 128 bits, 32 bits, and 64 bits, respectively. In addition, hash functions and symmetric key encryption require 160 bits and 256 bits, respectively. Finally, the modular operation and the scalar multiplication operation on the elliptic curve define 1024 bits and 320 bits, respectively.  
Table 6 tabulates the analysis results of the communication costs. In Figure 4, the transmitted messages require \((160+160+160=640\) bits), \((160+160+160+128+160+160=1088\) bits), \((160+160+160=480\) bits), and \((160+160=320\) bits). Consequently, the total communication cost of our protocol was 3136 bits. Although the proposed protocol had a higher communication cost than [PERSON] et al.'s protocol [16] and it provided better security than [PERSON] et al.'s scheme [16].
## 5 Numerical Simulations
### RANS Simulations
#### 5.1.3 Validation  
RANS simulations in uniform inflow were run at different wind speeds to validate turbine thrust and power. The results were compared to performance data given by the manufacturer and found in literature and are given in Figures 8 and 9. It can be seen in these figures that both thrust and power are modeled accurately. In Figure 9b, it can be seen that at a wind speed of 10 m/s, the thrust is overestimated. This is likely because the turbine would approach rated wind speed and go into a different operating region, which would mean altering the TSR. Tip speed ratio for the V80 turbine is \(TSR=7.3\), and for the Samsung it is as described earlier in Section 2.  
LES data from [PERSON] et al. [7] of a Vestas V80 turbine was used to validate the wake of the RANS case. [PERSON] et al. [7] used a hub-height wind speed of \(U_{hub}=8\) m/s in an unstable atmosphere with turbulence intensity \(TI=6.16\%\). A shear profile for the RANS simulation was generated with the power law using \(\alpha=0.019\) as found by [PERSON] et al. [30] for an unstable atmosphere; see Table 4. Another parameter with significant impact is the turbulent dissipation rate \(\varepsilon\) prescribed at the inlet. It is based on a turbulent length scale \(Tu_{L}\)  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Thrust [kN]** & **Power [MW]** \\ \hline Vestas\({}_{medium}\) & 146 & 0.791 \\ Vestas\({}_{fine}\) & 148 & 0.809 \\ Vestas\({}_{Diff}\) & 1.37\% & 2.28\% \\ \hline Samsung\({}_{medium}\) & 790 & 3.58 \\ Samsung\({}_{fine}\) & 803 & 3.74 \\ Samsung\({}_{Diff}\) & 1.57\% & 4.40\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Turbine thrust and power for RANS refinement study.  
Figure 6: V80 velocity deficit at different downstream locations in uniform and sheared inflow.  
Setting \(Tu_{L}=9.5\) gave the best agreement with validation data (see Figure 10a) and will be used in further simulations. The Samsung turbine was validated with LiDAR data from the TotalControl project [31], where measurements are done on the Levenmouth turbine operated by ORE Catapult. A rather large turbulent length scale of 85.6 m, or \(1R\) (1 Radius), gave a solution most similar to that of the validation data, as shown in Figure 10b. It should be noted that the data used to validate this turbine is quite noisy, and there is an absence of information describing the inflow conditions when the measurements were made. With a basis in the information given from the TotalControl project, a wind speed of 8 m/s and a turbulence intensity of 10% were therefore assumed, while the shear profile was imposed in a similar manner to that of the Vestas turbine.  
Figure 8: Simulated (**a**) power and (**b**) thrust for the Vestas turbine plotted against curves given by the manufacturer.  
Figure 7: Shear profiles plotted for locations downstream of the inlet in the V80 RANS simulation used for validation.
# Encoder-Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation
## 2 Materials and Methods
### Semantic Segmentation Model
#### 2.1.4 The Decoder Module  
The function of the decoder module was to gradually restore the resolution of the output feature map, including three up-sampling structures, the detailed structure of which is shown in Figure 6. Inspired by ReNet [29], we used skip connections to take the input features (the red arrow in Figure 1) and the output features from the previous layer as inputs to our up-sampling structure so that the model could learn the residual and avoid the loss of information. We applied a 1 \(\times\) 1 convolution operation and a bilinear interpolation operation on the two branches, with the upper branch containing a BN layer and a PReLU [30] layer. Finally, we summed the results of the two branches and output the feature map through a BN layer, a PReLU layer and a 3 \(\times\) 3 convolutional layer. We must clarify that the third up-sampling module in the decoding module was different from the first two, mainly using four-time interpolation to keep the same size of the input RGB images as the output semantic segmentation result.
## 3 Results and Discussion
### Acceptable Macro-Environment  
The upper limit of acceptable macro-environmental conditions was determined using the trial-and-error method within the searching loop. The macro-environment can be relaxed to an upper limit of 33-65 %RH with a \(\pm\)16 %RH fluctuation over 24 h, while the current requirement is 40-60 %RH with a \(\pm\)10 %RH fluctuation.  
Correspondingly, the micro-environment RH can be maintained within the range of 43-57.3 %RH, with 24-h fluctuation of \(\ll\)\(\pm\)9.1 %RH, as shown in Figure 10. These results illustrate that the buffering capacity of the enclosure ensures a stable micro-environment with low RH fluctuation amplitude. This stability opens up the potential to relax current tight control in the macro-environment without any detrimental effect on the collections.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Tigh-Control** & \(\uparrow\downarrow\)**5 °C@50 \%RH** & \(\uparrow\downarrow\)**10 \%RH @20 °C** \\ \hline KGE for temp. & 0.51 & 0.84 & 0.97 \\ KGE for RH & 0.58 & 0.77 & 0.63 \\ \hline \end{tabular}
\end{table}
Table 3: KGE values for micro-environmental temperature and RH.  
Figure 9: Comparison between measured data and simulated data. (**a**) 24 h data in tight control; (**b**) \(\uparrow\downarrow\)5 °C fluctuation during 60 min period at fixed \(\sim\)50 %RH; (**c**) \(\uparrow\downarrow\)10 %RH fluctuation during 60 min periods at fixed \(\sim\)20 °C.  
While only a single amplification loop was used, the assumption of data amplification across key frequency components serves as the worst-case scenario, thereby offering a conservative estimate of risk. In real-world applications, environmental fluctuations are typically less severe and less synchronized. Therefore, if the enclosure system proves effective under such amplified conditions, its performance under actual, more moderate variability can be considered robust. Nonetheless, future work should examine the sensitivity of this threshold under stochastic noise or uncertainties in the input data to further validate statistical robustness.
# Safety Concept for Textile-Reinforced Concrete Structures with Bending Load
## 1 Introduction and Objective  
Textile-reinforced concrete (TRC) is an innovative composite material that uses mesh-like reinforcements made of alkali-resistant glass (AR-glass) or carbon. In contrast to ordinary steel reinforcements, the textile reinforcements do not corrode. As a result, the concrete cover can be minimized, which leads to extremely thin and slender concrete constructions. The latest developments show that, due to the higher tensile strength and the higher elastic modulus, carbon filaments are used more often to realize constructions. As a consequence, the new name \"carbon concrete\" has appeared [1; 2; 3].  
This paper aims to determine a general partial safety factor \(\gamma_{\mathrm{T}}\) for the textile reinforcement of concrete structures with bending load through the calculation of a probability of failure \(p_{\mathrm{f}}\) (or the corresponding reliability index \(\beta\)). Several researchers have studied the bending capacity of textile-reinforced concrete members [4; 5]. However, in the design of bending reinforcement of textile-reinforced members, the partial safety factors for the textile reinforcement have not been analyzed in detail.  
The bending load capacity depends on different parameters such as the ultimate tensile strength of the textile reinforcement \(f_{\mathrm{t}}\) or the concrete compressive strength \(f_{\mathrm{c}}\). These parameters vary according to the application field and, therefore, have a direct influence on the resulting reliability index \(\beta\)This influence can be determined through sensitivity factors \(\alpha_{i}\), that can be selectively considered to increase the safety level of the structural element being analyzed. Furthermore, the partial safety factor \(\gamma_{T}\) can vary within the reliability analysis in order to attain the desired reliability index \(\beta\).  
The results of the reliability analysis depend on the probability distribution allocated to the variable parameters. The more realistically the probability distributions are estimated, the more accurate are the respective probabilities of failure. In some cases, such as for the textile ultimate strength\(f_{t}\), the probability distributions can be determined with acceptable effort. Other distributions, especially on the impact side, tend to be more complex to determine. Various models can be found in the literature, but they differ considerably [6; 7].  
For the reliability analysis, the first-order reliability method (FORM), the Monte Carlo method (MC) and the Monte Carlo method with importance sampling (MC-IS) were used. The calculations were performed with the software Maple [8].  
In this paper, all the varying parameters are listed; however, the influence of the relevant parameters is illustrated with selected results. Further results of the parameter calculations can be found in [9]. The abbreviations and symbols used in this study are also described in detail in [9]. The results of this paper were originally published in German [10].
## 3 Parallel Computing Based on a Hybrid OpenMP-MPI Approach
### Parallelization Strategy Based on Hybrid OpenMP-MPI Approach
It is worth noticing that it is not trivial to accomplish OpenMP-MPI paradigm, since three critical issues arise, as follows:  
1. Need for local thread memory-- the main loop is distributed among all available threads, each of them requesting its local memory to work properly. This memory is related to mesh element information (e.g., the curls of the elements, the shape functions, geometrical information, element local output, and so on). Anyway, it is usually very small (few GBs) and, in cases of practical interest, is much smaller than the required output global node memory. 2. Global matrix memory update--once the single thread made its own job, the thread local output should be accommodated into the global memory of the node. This is a non-trivial operation, and it is a bottleneck for the method, because the local update must be carried out by each thread in a sequential access, so to guarantee consistency. To this end, the CRITICAL OpenMP directive is used [6]. 3. Global input memory access--this access is critical being shared between the threads. It may cause \"cache missing\". Of course, all of the above issues cause a degradation of the performances; moreover, as we can see in Section 4, they do not significantly affect the obtained speed-up parameters. Let us now describe the changes introduced by the proposed hybrid approach in the algorithms for assembling the matrices and vectors of the Numerical Problem (6). In building the known-terms vector \(\mathbf{V}_{0}\), a hybrid implementation of the external loop (over the mesh elements) is handled by all threads. The local memory V0_loc is automatically obtained by the means of OpenMP reduction operations (Algorithm 4). The new algorithm to be used to assembly the matrix \(\mathbf{L}\) is described in Algorithm 5. In the MQS applications, such as those analysed in this paper, the fully populated matrix \(\mathbf{L}\) is by far the largest quantity. The entries of this matrix are arranged in a 2D cyclic block size fashion, in view of using the Scalapack inversion routine [24; 26]. To this end, the entries MdL are reorganized in \(M_{LOC}\) in the last step (final communication step). Once again, we stress the advantage of the hybrid approach over the pure MPI approach: since it is usually \(N_{N}\approx N_{MPI}\), the memories MdMesh and MdL (by far the largest ones) are allocated only once in the node, instead of being allocated for each process in the pure MPI approach. Finally, the \(\mathbf{Q}\) matrix can be very large if the computation of the flux density is required in a huge number of points and/or in each element of the mesh. With the hybrid approach, the external element loop is distributed among the threads in the current MPI process, see Algorithm 6.
# Study of the Impact of PV-Thermal and Nanofluids on the Desalination Process by Flashing
## Numerical Modeling
### Multistage Flashing Chamber Model  
In this section, we present a mathematical and numerical modelling on the use of nanofluids and PV-thermal solar to power the multistage flashing chamber thermal (MSF) process, as shown in Figure 1. In this figure, the brine is preheated by the nanofluid circulating in the PV-thermal loop where the heat dissipated from the PV solar panel is absorbed by the circulating heat transfer fluid, as shown in the figure. The flashing process occurs in the flashing chamber where the flashed vapor is generated and condensed on the heat exchanger that is cooled down by the incoming brine flow. The distilled condensed vapor is collected and the brine flow is circulated to the next flashing chamber where the same flashing process is repeated. The model proposed was established after the mass and energy equations coupled with the state equations of the nanofluids and water as the base fluid at different solar radiations. This numerical process was repeated for each of the flashing chambers. In the following section, we present the mass and energy equations of the brine flow in the flashing chamber.  
Each flashing chamber is considered as discrete control volume and has a constant volume; therefore, by applying the first law of thermodynamic to the control volume, the mass balance and energy balance across the flashing chamber can be given by the following equations, respectively [7]:  
\[\Sigma m_{in}-\Sigma m_{out}=0 \tag{5}\]  
\[\Sigma e_{in}-\Sigma e_{out}=0 \tag{6}\]  
where \(m\) and \(e\) are the mass and energy entering and leaving the flashing chamber control volume.  
The brine sea water mass balance across the preheater is  
\[m_{Bi}-m_{Bo}=0 \tag{7}\]  
where \(m_{CBi}\) and \(m_{Bo}\) are the brine mass flow entering and leaving the preheater, respectively.  
The brine concentration entering and leaving are given by the mass balance equation:  
\[m_{Bi}S_{Bi}-m_{Bo}S_{Bo}=0 \tag{8}\]  
Figure 1: PV-Thermal integrated flashing chamber process.  
The distilled condensed mass balance is  
\[m_{Co}-m_{Ci}-m_{Dbrine}=0 \tag{9}\]  
where \(m_{Dbrine}\) is the distilled mass of vapor condensed as per Figure 1.  
The preheater energy balance is  
\[(m_{Dbrine}+\gamma m_{Bi})\Delta H_{eup}-m_{Bi}Cp_{brine}(T_{CBo}-T_{CBi})=0 \tag{10}\]  
where the distillate flashing fraction \(\gamma\) is  
\[\gamma=\frac{Cp_{cond}*DT_{Stage}}{\Delta H_{eup}} \tag{11}\]  
where \(DT_{Stage}\) and \(\Delta H_{eup}\) are the stage temperature difference and heat of evaporation, respectively.