esa-sceva/satcom-chunk-collection huggingface dataset:
# Computational Evaluation of Shock Wave Interaction with a Liquid Droplet
## 3 Results  
The computational setup described in Section 2.1, without the presence of the water droplet, practically corresponds to the classical shock tube problem [34], which admits an exact inviscid solution and has become a standard test case for compressible flow solvers. Therefore, while demonstrating the post-shock air flow in which the liquid droplet is immersed, the initial simulation of the empty shock tube device also represents a preliminary verification and validation test to be completed, e.g., [10].  
Here, the robustness of the present CFD model and the accuracy of the transient numerical solution of the shock tube problem were assessed through comparison with the corresponding analytical discontinuous solution [24]. Specifically, the profiles along the shock tube of different flow field variables at a time instant, say \(t_{0}=2d_{0}/V_{\mathrm{S}}\), when the incident shock impacts the droplet, were examined. By looking at the pressure, temperature, density and velocity plots that are reported in Figure 2, for the three different mesh resolutions, the comparison between numerical and analytical solutions is quite satisfactory. Note that, as it happens in real flows, theoretical discontinuities are smoothed out by resolving viscous effects in practical simulations. Based on the present grid sensitivity analysis, as well as other similar studies using the VOF methodology, the spatial resolution associated with the solution CFD III can be assumed fully adequate to capture the salient flow features without being computationally cumbersome.  
Figure 2: Profiles of pressure (**top left**), temperature (**top right**), density (**bottom left**), and velocity (**bottom right**) along the shock tube at the impact time instant, compared to the exact inviscid solution (black solid line), for three different solutions that are CFD I (dotted green line), CFD II (red dash-dotted line) and CFD III (dashed blue line).  
The above preliminary simulation allows us to confirm the overall characteristics of the case study described in Section 2.1. In particular, the expansion waves are demonstrated to travel in the opposite direction with respect to the shock front and thus, do not influence the SW/droplet interaction. Subsequently, the complete setup was employed to simulate the interaction of a single water droplet with a traveling SW, where distortion and successive breakup of the water body are observed, as a consequence of the impact of the shock front. In the following discussion, as is usually done in the relevant literature [2; 7], the following nondimensional group  
\[t^{*}=\frac{V_{2}}{d_{0}\sqrt{\varepsilon}}(t-t_{0}) \tag{8}\]  
was used as normalized time variable for examining the droplet aerobreakup.
# Low Carbon Optimization of Deep Foundation Pit Support in Undulating Strata
## 4 Calculation Results Comparison Analysis
### Theoretical Design Calculation Results Analysis
#### 4.1.1 Foundation Pit Deformation Analysis  
The theoretical calculation of foundation pit settlement is mainly calculated by the parabolic method, the triangular method, and the exponential method. The results calculated by different methods are different. In the actual design, in order to ensure the safety of construction, the maximum value is taken as the design calculation. The results of the deep foundation pit are shown in Figure 5.  
As shown in Figure 5, the maximum result calculated using the exponential method is 55 mm, while the result calculated using the triangular method is 37 mm, and the result calculated using the parabolic method is 29 mm. The settlement values of the deep foundation pit ground calculated by the three methods are within the range of 0.2% H (56 mm) allowed by the specification, but the settlement of the elastic method is too close to the standard. During the construction process, the reinforcement of the ground settlement of the long side wall should be strengthened as well.  
In the analysis of the horizontal displacement of the foundation pit's sidewall, the elastic method was employed to design the support structure. Based on the foundation pit's ultimate working condition, it was determined that the maximum horizontal displacement of the support structure anchored with piles is 33.94 mm. This displacement value satisfies the requirement of the first-level deep foundation pit, which is set at 0.3% H.
###### Abstract  
In the 21 st century, mega-constellations and interconnected satellite constellations deployed at various orbital altitudes, such as LEO, MEO, and GEO, with low Earth orbits (LEOs) being the most commonly used, have emerged as a trend, aiming to enhance the productivity and reduce the costs in space service delivery. The UNOOSA has noted the uncertainty in the exact number of satellites but conducted simulations based on a substantial sample, projecting a significant increase from the 2075 satellites recorded in orbit in 2018. This surge in the launch of mega-constellations poses profound challenges to existing international space laws, originally formulated with limited consideration for private space actors, who are increasingly engaging in space activities, particularly with the cost-effective utilization of mega-constellations. This study critically analyzes the compatibility of mega-constellations with the current international space laws by examining the applicability of mega-constellations concerning equitable access and the non-appropriation principle, addressing their potential occupation of substantial orbital spaces during activities, and analyzing whether the acquisition of orbital slot licenses violates these two principles. Following an in-depth analysis, this study proposes recommendations to amend the existing laws, aiming to resolve ambiguities and address emerging challenges. Recognizing the time-consuming process of amending international space laws, this study suggests practical recommendations for supplementary rules of the road, prompting reflection on the potential obsolescence of the current international space laws in the face of evolving space activities.  
mega-constellations; international space law; equitable access; non-appropriation Article Assessing the Principle of Equitable Access versus Non-Appropriation in the Era of Mega-Constellations  
1
## The Living Building Blocks of MESs
### Pilin-Mediated Extracellular Electron Transfer (PEET)  
_G. sulfurreducens_ is also capable of electron transfer over longer ranges via electroconductive pili [55; 56]. The presence of OmcS aligned along the length of pili has been suggested as one mechanism increasing conductivity for electron transfer [52]. The use of pili as a conductive electron transfer network throughout _G. sulfurreducens_ biofilms has been suggested based on the observation of relatively thick biofilms being electroconductive [56; 57]. Furthermore, the conductive nature of _G. sulfurreducens_ pili has been demonstrated through the replacement of its PilA with that from _Pseudomonas aeruginosa_, resulting in decreased conductivity and the ability to reduce Fe(III) Oxide [58]. The metallic-like conductive nature of pili has, however, been a controversial topic, with differing interpretations of data resulting in different hypotheses [59; 60; 61; 62; 63].The alternative hypothesis, known as the super exchange model of electron transfer, has also been proposed, in which electrons are proposed to successively transfer between redox proteins via a \"hopping\" mechanism [64]. However, this hypothesis is argued to be less suited to current knowledge surrounding biofilm conductivity than the hypothesis of a metallic-like conductivity of pili [62; 64; 65]. Subsequent investigation has further supported the metallic-like conductivity of _G. sulfurreducens_ pili based on the structural arrangement of aromatic amino acids of the pili [60]. More recently, the expression of electroconductive PilA from _G. metallireducens_ by _G. sulfurreducens_ has been found to improve conductivity of pili beyond that of any other EAM currently known [66]. Interestingly, this model for pili conductivity is different to that seen in _S. oneidensis_, in which it is more likely suggested that electrons hop between redox sites along membrane extensions [67; 68]
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 2 Materials and Methods
### Basic System Structure  
Our system is divided into functional blocks with the modulation and demodulation in focus, as shown in Figure 1; more details about the structure inside those two blocks is discussed in Sections 2.2 and 2.4.  
The data \(d\) of length \(N\) is modulated into the digital sequence of linear up and down chirps \(y_{\text{tx}}\), now of length \(N_{\text{tx}}\), as illustrated in Figure 1. The DAC then converts this into the the output \(s_{\text{tx}}\), which is an analog continuous real-valued signal of length \(T_{\text{tx}}\) that is boosted by a power amplifier (PA) before it is turned into an acoustic wave by a piezoelectric transducer.  
The received signal is bandpass-filtered and amplified by an analog active filter (AF) into the continuous real-valued and band-limited received signal \(s_{\text{rx}}\) of length \(T_{\text{rx}}\). The ADC samples the received signal into the sequence \(y_{\text{rx}}\) of length \(N_{\text{rx}}\). The demodulation step  
Figure 1: Flow diagram of the basic communication chain: The data \(d\) is modulated, amplified before transmission, filtered and amplified at reception, and demodulated as \(d_{\text{est}}\). The entire analog domain is regarded as part of the communication channel.  
estimates the originally sent sequence as \(d_{\text{est}}\) given a small set of prior information about the original signal, e.g., the ideal chirp parameters.  
We consider exclusively time discrete signals throughout this work, sampled at points  
\[n=\lfloor t\,f_{\text{s}}\rfloor\in\mathbb{Z}, \tag{1}\]  
where \(t\) is the time of observation and \(f_{\text{s}}\) the sampling frequency. For simplicity we assume the sampling frequency of transmitter and receiver to be equal, save an oscillator frequency mismatch of \(\Delta f_{\text{s}}\) and phase offset of \(\Delta\phi_{\text{s}}\). In practical application, this is not required, but only the parameters that describe the used waveform sufficiently.
## 5 Results
### Dynamic Analyses
These data indicate that the implementation of ACPH technology leads to a proportional decrease in the base shear allocated to the middle column, thus facilitating a more equitable distribution of shear among the frame's columns. The findings illustrated in Figures 12-14 collectively underscore the pronounced disparities in damage distributions between frames characterized by traditional RC joints and those integrated with the advanced energy dissipating ACPH system. In the reference RC frames, both compression and tension damage indices approach or exceed the threshold of 0.85 in proximity to the beam-column connections, particularly at the extremities of the beams where the formation of plastic hinges is typically observed. These regions are also associated with the highest levels of plastic strain, indicating extensive cracking, the  
Figure 11: Comparison of base shear time-history responses for four precast frame configurations: (**a**) 1B1S, (**b**) 1B2S, (**c**) 2B1S, and (**d**) 2B2S. Conversely, the frames equipped with the ACPH system demonstrate a significantly divergent damage pattern. The indices for both compression and tension remain considerably lower, typically below 0.3 across most concrete areas, including the critical joint zones. The plastic strain distributions reveal that inelastic deformation is sharply concentrated within the steel plates of the ACPH, particularly in the 3 mm Q235 fuse zones. In these areas, strain levels indicative of yielding is attained, while the surrounding concrete and longitudinal reinforcement largely remain within the elastic domain. These disparities substantiate that the ACPH mechanism effectively diverted inelastic demand away from the RC frame and directed it towards the designated sacrificial component. The hinge adeptly performed its role by localizing energy dissipation, thereby reducing structural damage and maintaining the integrity of the primary load-bearing framework. This confinement of damage represents a crucial performance criterion for resilient seismic design, as it facilitates post-event reparability and mitigates the risk of progressive damage throughout the frame.
# Efficient Sizing and Layout Optimization of Truss Benchmark Structures Using ISRES Algorithm
###### Abstract  
This paper presents a comprehensive investigation into the application of the Improved Stochastic Ranking Evolution Strategy (ISRES) algorithm for the sizing and layout optimization of truss benchmark structures. Truss structures play a crucial role in engineering and architecture, and optimizing their designs can lead to more efficient and cost-effective solutions. The ISRES algorithm, known for its effectiveness in multi-objective optimization, is adapted for the single-objective optimization of truss designs with multiple design constraints. This study encompasses a wide range of truss benchmark structures, including 10-bar, 15-bar, 18-bar, 25-bar, and 72-bar configurations, each subjected to distinct loading conditions and stress constraints. The objective is to minimize the truss weight while ensuring stress and displacement limits are met. Through extensive experimentation, the ISRES algorithm demonstrates its ability to efficiently explore the solution space and converge to optimal solutions for each truss benchmark structure. The algorithm effectively handles the complexity of the problems, which involve numerous design variables, stress constraints, and nodal displacement limits. A comparative analysis is conducted to assess the performance of the ISRES algorithm against other state-of-the-art optimization methods reported in the literature. The comparison evaluates the quality of the solutions and the computational efficiency of each method. Furthermore, the optimized truss designs are subjected to finite element analysis to validate their structural integrity and stability. The verification process confirms that the designs adhere to the imposed constraints, ensuring the safety and reliability of the final truss configurations. The results of this study demonstrate the efficacy of the ISRES algorithm in providing practical and reliable solutions for the sizing and layout optimization of truss benchmark structures. The algorithm's competitive performance and robustness make it a valuable tool for structural engineers and designers, offering a versatile and powerful approach for complex engineering optimization tasks. Overall, the findings contribute to the advancement of optimization techniques in structural engineering, promoting the development of more efficient and cost-effective truss designs for a wide range of engineering and architectural applications. The study's insights empower practitioners to make informed decisions in selecting appropriate optimization strategies for complex truss-design scenarios, fostering advancements in structural engineering and sustainable design practices.  
truss optimization; ISRES; sizing optimization; layout optimization; structural engineering
## 2 Material and Methods
### Flux Data Processing and Gap Filling  
The daily summary logged files containing 30 min flux values were collected and stored as tab-delimited text files. The REddyProc Software Package from the Max Planck Institute for Biogeochemistry was used for flux data gap-filling and partitioning of net ecosystem exchange (NEE) into ecosystem respiration (ER) and gross primary production (GPP). In particular, the night-time based algorithms [13] were used for NEE partitioning; and the Ustar-based methods in conjunction with the Moving Point Test threshold estimation algorithms [14] were used for flux data filtering. The detailed information regarding the data filtering, gap-filling, and partitioning [13; 15] could be found at the REddyProc website: [[https://www.bgc-jena.mpg.de/bigi/index.php/Services/REddyProcWebRPackage](https://www.bgc-jena.mpg.de/bigi/index.php/Services/REddyProcWebRPackage)]([https://www.bgc-jena.mpg.de/bigi/index.php/Services/REddyProcWebRPackage](https://www.bgc-jena.mpg.de/bigi/index.php/Services/REddyProcWebRPackage)) (accessed date: 24 January 2021). According to the sign convention for presenting NEE data, negative flux values indicate overall CO\({}_{2}\) sequestration (fluxes from the atmosphere to the ecosystem) and positive values indicate overall CO\({}_{2}\) release (fluxes from the ecosystem to the atmosphere). Throughout the entire duration of this study, 16.5% of NEE data and 15.9% of latent heat flux (LE) data were classified as \"gap\" either caused by missing values or identified by the Ustar-filtering algorithms. Evapor transpiration (ET) was calculated using LE based on equations described by [PERSON] et al. [16]. Obvious outliers (NEE beyond \(\pm\)50 umol m\({}^{-2}\) s\({}^{-1}\)) [17; 18] were removed manually from the dataset. All daily cumulative values, including NEE, ER, GPP, and ET; were calculated by integrating the value-by-seconds plotting using the \"trapz\" function (trapezoidal numerical integration) in MATLAB.  
The energy balance and closure of the entire ecosystem were evaluated by regressing the 30 min values of total turbulent fluxes (H + LE) with the available energy accumulation [net radiation (Rn)-soil heat flux (G)] within each growing season. Specifically, non-gap-filled high-quality data, indicated by '0' quality flag values for corresponding LE and H, and those ranging from \(-\)200 to 500 and from \(-\)200 to 800 W m\({}^{-2}\) of H and LE, respectively; were retained for this task [19; 20]. After applying all filtering criteria mentioned above, 28% of data were removed from the energy balance/closure analysis. Finally, both coefficient of determination (R\({}^{2}\)) and root mean square error (RMSE) were used for model fitting assessment and the results were presented by growing season.
# Total Ionizing Dose Effects on a Delay-Based Physical Unclonable Function Implemented in FPGAs
## 5 Conclusions  
RO-PUFs leverage minor delay variations that exist between devices to support security functions such as authentication and key generation. As TID significantly affects propagation delays, the response of an RO-PUF may be jeopardized in space. In this work, we have performed a comprehensive analysis of the effects of ionizing radiation on the quality of RO-PUFs implemented in FPGAs. RO-PUFs responses were collected as dose accumulated in order to evaluate uniformity, reliability, uniqueness and frequency stability. The external environment was controlled and the core currents and voltages were continuously measured to ensure the results were not biased.  
Experimental results showed that RO frequencies show good stability and that the randomness of the response is not affected by TID. However, the reliability and uniqueness of the response shows a little degradation. Nevertheless, this degradation can be corrected by using some typical countermeasures. With these corrections, we can conclude that RO-PUFs implemented in FPGAs can be used in space applications.  
Conceptualization, H.M. and L.E.; Methodology, H.M., P.M.-H. and Y.M.; Writing--Original Draft Preparation, H.M., P.M.-H., Y.M., L.E. and E.S.-M.  
This work was partially supported by the Spanish Ministry of Economy and Competitiveness under contracts ESP2015-68245-C4-1-P and ESP-2015-68245-C4-4-P.  
Thanks to the ALTER TECHNOLOGY company for making possible the monitoring and recording of the biasing and environmental test conditions during the irradiation test runs.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 3 Development of an HS-ANN Hybrid Model
### Sampling of Training Data of ANN Model
The surf similarity parameter was split into cot\(\alpha\), \(H_{s}\), and \(T_{p}\) as done by [PERSON] and [PERSON] [6]. The peak period, \(T_{p}\), was used instead of \(T_{m}\) because it contains the information about spectral peak as well as mean wave period. The neural network can deal with qualitative data by assigning the values to them. The permeability coefficients of impermeable core, permeable core, and homogeneous structure are assigned to 0.1, 0.5, and 0.6, respectively, as done by [PERSON] [27]. On the other hand, the spectral shapes of narrowband, medium-band (_i.e._, Pierson-Moskowitz spectrum), and wideband are assigned to 1.0, 0.5, and 0, as done by [PERSON] _et al._[3]. To perform the chi-square test, the range of each variable was divided into eight to 11 sub-ranges. The details of the test can be found in the thesis of [PERSON] [31]. Here, only the residual chart calculated based on Equation (4) is presented in Table 3. Some variables are well distributed over the whole range, whereas some varies among a few sub-ranges (e.g., \(P=0.1\), 0.5, or 0.6). Table 3 shows that Equation (4) is satisfied for all the variables, indicating that the probability mass function of each variable of the training data is significant at a 5% level of significance. As an example, the probability mass functions of the training data and population of the damage level \(S\) are compared in Figure 3, showing that they are in good agreement. \begin{table}
\begin{tabular}{c c} \hline
**Input Variables** & **Output Variable** \\ \hline _P, N\({}_{w}\), S, cot\(\alpha\), \(H_{s}\), T\({}_{p}\), \(h/H_{s}\), SS_ & \(N_{s}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Input and output variables. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Range** & \(N_{s}\) & \(P\) & \(N_{w}\) & \(S\) & **cot\(\alpha\)** & \(H_{s}\) & \(T_{p}\) & \(h/H_{s}\) & \(SS\) \\ \hline
1 & 0.09 & 0.26 & 0.03 & 0.42 & 0.13 & 0.06 & - & - & 0.31 \\
2 & 0.00 & - & - & 0.03 & - & 0.00 & - & 1.74 & - \\
3 & 0.00 & - & - & 0.01 & 0.00 & 1.24 & - & 0.00 & - \\
4 & 0.00 & - & - & 0.01 & - & 0.14 & 0.84 & 0.17 & - \\
5 & 0.05 & - & - & 0.12 & 0.47 & 0.11 & 0.00 & 0.08 & 0.00 \\
6 & 0.10 & - & - & 0.14 & - & 0.02 & 0.02 & 0.02 & - \\
7 & 0.04 & - & - & 0.11 & - & 1.24 & 0.00 & 0.06 & - \\
8 & 0.07 & 0.35 & - & 0.06 & - & - & 0.02 & 0.00 & - \\
9 & 0.14 & - & - & 0.90 & - & - & 0.38 & - & - \\
10 & 1.50 & 0.04 & 0.03 & 0.45 & 0.08 & - & - & 0.14 & 0.37 \\
11 & - & - & - & 0.43 & - & - & 0.52 & - & - \\ \(\sum{(n_{i}-e_{i})^{2}}/{c_{i}}\) & 1.99 & 0.64 & 0.06 & 2.67 & 0.68 & 2.81 & 1.77 & 2.20 & 0.69 \\ \(f\) & 9 & 2 & 1 & 10 & 3 & 6 & 6 & 7 & 2 \\ \(c_{1-\sigma,f}\) & 16.8 & 5.99 & 3.84 & 18.3 & 7.86 & 12.6 & 12.6 & 14.1 & 5.99 \\ \hline \end{tabular}
\end{table} 
Table 3: Residual chart of chi-square tests. Figure 3: Probability mass functions of damage level \(S\): (**a**) population; (**b**) training data.