esa-sceva/satcom-chunk-collection huggingface dataset:
# Large Scale Imaging by Fine Spatial Alignment of Multi-Scanning Data with Gel Cube Device
## Materials and Methods  
### Fabrication of the Gel Cube Device with Alignment Mark  
A cubic polycarbonate (PC) frame, 3 mm on each side, was machined by drilling and grinding. The thickness of the frame was 0.5 mm. Then, microstructure fabricated by photolithography was attached to the vertex of the PC frame by polydimethylsiloxane (PDMS). The frame was placed on a precooled glass slide, and 4.5 \(\mu\)L of 1.5% agarose (A9414; Sigma-Aldrich, St. Louis, MO, USA) was dropped inside the bottom frame to form a gel. The frame was subsequently rotated, and the other five surfaces were covered with gel. Karl Zeiss LSM-700 or Keyence BZ-X 700 sectioning module was used for \(z\)-stack imaging.
## 3 Mosaik Co-Simulation Setup for Transient Stability Assessment
### Study Steps to Setup the Co-Simulation Framework
#### 3.1.1 Model a Monolithic Reference Case in Powerfactory  
The first step is to model a power system including a wind turbine (Figure 4) in Powerfactory. A common droop-based grid-forming control is considered for the wind turbine controller. Details on grid-forming control will be explained in the next section. After model preparation, a monolithic transient stability simulation is performed. In the monolithic reference case, the wind turbine can successfully ride through a 150 ms self-cleared three-phase short circuit at the busbars of the grid terminal. This is referred to as the _reference fault_ throughout this paper. Under most grid conditions, reactive current provision can support the voltage during fault-induced dips [51, 52]. According to most present grid codes, wind turbine generators shall remain connected during reduced voltage conditions for up to 150 ms and simultaneously inject 1 p.u. of reactive current ([53]).
# Regeneration of Activated Carbons Spent by Waste Water Treatment Using KOH Chemical Activation
## 3 Results and Discussion
The toluene adsorption capacities on the ACs were measured using the experimental setup as shown in Figure 1. The surface area, pore size, and functional groups on the surfaces of ACs are considered as key factors to affect adsorption mechanisms, leading to the various toluene adsorption performance [30; 31; 32; 33]. First, the toluene adsorption capacities were largest at a higher surface area: C-AC-A \(>\) C-AC-W \(\approx\) H-AC \(>\) S-AC, and the calculated adsorption capacity was 0.154, 0.091, 0.100, and 0.033 g-toluene/g-AC, respectively (Table 5). The toluene adsorption capacity calculated the integrated area divided by sample weight. In the previous reports [30; 31], pore size distribution is also a key parameter to control the toluene adsorption. The toluene is usually adsorbed on the mesopore of ACs, and then diffused into the micropores. The mixture structure of micro- and meso-pores enhances their toluene adsorption capacity. If the pores are composed of only microstructures or too narrow, the adsorption capacity is decreased. The pores of ACs by chemical activation (C-AC-A) were composed of micropores (pore diameter \(<\)2 nm) and meso-pores (2 nm \(<\)pore diameter \(<\)50 nm) as shown Figure 2b, so it enhanced the toluene adsorption capacity. In addition, the functional group on the ACs can affect the toluene adsorption [33]. The ACs with low content in oxygen surface groups have the best adsorption capacities for VOCs. Based on our EA result in Table 3, the S-AC exhibited high content in oxygen surface groups compared with H-AC. It caused to the significantly low toluene capacity, even comparing with their surface areas. The commercial AC based on coal (889 m\({}^{2}\)/g) was prepared, and also applied to the toluene adsorption test. The calculated adsorption capacity was 0.142 g-toluene/g-AC. The toluene adsorption capacity of C-AC-A is higher than commercial ACs. Therefore, the regenerated ACs from the waste in this study exhibits better physical properties  
Figure 5: X-ray fluorescence (XRF) analysis of the chemical composition of ACs. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & \\ \cline{2-10}  & **Ca** & **Si** & **Fe** & **S** & **K** & **Al** & **Mn** & **Ti** & **P** & **Cu** & **Cr** \\ \hline S-AC & 31.2 & 23.5 & 18.2 & 8.0 & 5.8 & 5.02 & 2.6 & 2.3 & 1.9 & 0.9 & 0.6 \\ H-AC & 29.1 & 29.2 & 12.6 & 9.4 & 6.9 & 6.27 & 2.1 & 2.1 & 2.3 & 0 & 0 \\
**C-AC-W** & 34.3 & 31.1 & 13.0 & 0.6 & 7.9 & 8.77 & 2.0 & 2.4 & 0 & 0 & 0 \\
**C-AC-A** & 3.7 & 21.2 & 44.2 & 4.8 & 16.0 & 1.6 & 0 & 6.0 & 2.6 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summarized XRF analysis of the ash of ACs. than commercial ACs, leading to the replacement of fresh ACs. It gives the environmental and economic benefits.
## 4 Experiments without Frame Aggregations
### Stationary Scenario  
In this subsection, we evaluate the performance of the proposed MAB algorithms in a stationary scenario. For the experiments in this scenario, we used two wireless nodes at fixed positions during the simulation time of 2.5 s. Both nodes use the IEEE 802.11g standard. During the simulation time, the sender transmitted CBR traffic of 54 Mbps to the receiver. The distance between the two nodes was fixed at 60 m. Because the two nodes were not moving and no other interfering nodes existed, we disabled the sliding window in the proposed MAB algorithms by setting \(\tau=2.5\).  
Because of the large distance of 60 m between the two nodes, as shown in Figure 1b in Section 4.1, the lowest MCS level (i.e., MCS 0) had higher packet success rates than the other MCS levels. In particular, the packet success rates of the MCS levels higher than 2 were almost zero with the distance. Thus, MCS 0 is the best MCS level in this scenario that can be selected by the sender. Figure 2 shows the experimental results when four different MAB algorithms were adopted by the sender.  
Figure 2a shows the changes in the application throughputs (0.1 s average) achieved by the receiver during a simulation time of 2.5 s. These results indicate that the application  
Figure 1: (**a**) Changes in packet success rate versus SINR with different MCS levels, (**b**) changes in packet success rate versus distance with different MCS levels in IEEE 802.11g networks.  
Figure 2: (**a**) Changes in application throughputs with different MAB algorithms, (**b**) changes in average MCS levels with different MAB algorithms in IEEE 802.11g networks.  
throughput can be approximately 5 Mbps in its maximum. The results show that TS-logR and KL-UCB-logR reached the maximum throughput faster than TS-R and KL-UCB-R, respectively.  
Figure 2b shows the changes in the average MCS levels (0.1 s average) selected by the four MAB algorithms. The results clearly show that the average of the MCS levels selected by TS-logR and KL-UCB-logR converged to MCS 0 faster than TS-R and KL-UCB-R, respectively. In addition, the results in Figure 2a,b show that TS-logR and KL-UCB-logR achieved a similar performance. However, TS-R had a better performance than KL-UCB-R in this scenario.
###### Abstract  
Currently, the technology of imparting the necessary reflective properties to a surface is becoming increasingly important. Darkening the surface and matting it helps to diffuse the reflected beam and prevent glare. The surface's reflective properties are determined by its microstructure. Modern pico- and femtosecond lasers make it possible to obtain surfaces with high precision and create various LIPSS (laser-induced periodic surface structure) types. In this article, we describe the process of formation of a complex two-periodic microstructure on the surface of AISI 321 stainless steel under the influence of radiation from femtosecond lasers and describe the process of scattering of a light beam by the resulting surface. Modeling shows that the presence of an additional transparent coating on a flat surface does not improve its scattering properties and does not eliminate glare. In the event that a complex two-periodic structure is formed on the reflective surface and the coating surface, the nature of the reflection has a clearly defined scattered character, regardless of the angle of incidence of the light beam. This study shows the feasibility and effectiveness of forming a two-periodic structure in order to give it stealth characteristics and reduce visibility.  
LIPSS; microstructure; femtosecond laser; reflective properties; stealthiness; stainless steel +
Footnote †: journal: Applied Physics and Chemistry Letters  
14 July 202417 September 2024  
the mechanism of formation of a complex two-periodic microstructure, and then computer models are built and used to carry out a comparative analysis of the nature of scattering of a narrow light beam on flat structured surfaces after laser modification and with an additional transparent coating. We aim to develop a surface microrelief with maximum scattering properties, which we believe can be practically obtained using laser processing.
## 4 Mission Reliability Modeling, Allocation, and Analysis Method Design
### Reliability Analysis Methodology Design
#### 4.3.2 Quantitative Calculation of Reliability Based on GO Method
\begin{array}{l}A_{R}(i)=A_{S1}(i)[P_{C}(0)+A_{S1}(i)\cdot P_{C}(1)],\; \mathrm{i}=0,\cdots,\mathrm{N}-1\\ P_{R}(N)=P_{S}(N)+A_{S1}(N-1)[P_{C}(2)+P_{S2}(N)\cdot P_{C}(1)]\\ P_{R}(0)=A_{R}(0)\\ P_{R}(i)=A_{R}(i)-A_{R}(i-1),\;\mathrm{i}=0,\cdots,\mathrm{N}-1\end{array}  \tag{22}\]  
Since there are common cause failures in the failure modes of the ship electronic information system, it is also necessary to modify the probability of state combinations characterized by common cause failures. Assuming that units \(A\) and \(B\) in the system have common cause failure, the system success probability can be modified as Equation (23) as follows:  
\[R=c_{0}+c_{1}Q_{A}+c_{2}Q_{B}+c_{3}Q_{A,B} \tag{23}\]  
where \(Q_{A}\) and \(Q_{B}\) are the failure probabilities of units \(A\) and \(B\), respectively; \(Q_{A,B}\) is the probability of simultaneous failure of the co-failure units; \(c_{0}\), \(c_{1}\), \(c_{2}\) and \(c_{3}\) are independent of units \(A\) and \(B\). Let the probability of co-causal failure of unit \(A\) and unit \(B\) be \(C_{A,B}\). The probability of failure of unit \(A\) and unit \(B\) are as follows:  
\[ \begin{array}{l}Q_{A}=Q_{A1}+C_{A,B}\\ Q_{B}=Q_{BI}+C_{A,B}\\ Q_{A,B}=Q_{AI,BI}+C_{A,B}\end{array}  \tag{24}\]  
where \(Q_{AI}\) and \(Q_{BI}\) are the failure probabilities excluding common cause failures, which are obtained by substituting into Equation (23):  
\[R=c_{0}+c_{1}Q_{AI}+c_{2}Q_{BI}+c_{3}Q_{AI,BI}+(c_{1}+c_{2}+c_{3})C_{A,B} \tag{25}\]  
It can be further rewritten as follows:  
\[R=R_{I}+C_{A,B}(R_{00}-R_{11}) \tag{26}\]where \(R_{I}\) is the system success rate without common cause failure; \(R_{00}\) is the system success probability calculated when common cause failure units \(A\) and \(B\) are included and the success rates of \(A\) and \(B\) are both 0; \(R_{11}\) is the system success probability when common cause failure units \(A\) and \(B\) are included and the success rates of \(A\) and \(B\) are both 1. For a complex system with several common cause groups, the probability of common cause failure for group \(m\) is evaluated as \(C_{m}\), which can be obtained by substituting into Equation (25) as follows:  
\[R=R_{I}+\sum_{m=1}^{M}C_{m}(R_{00}-R_{11}) \tag{27}\]  
where \(R_{00}\) and \(R_{11}\) are the system success probabilities obtained when the success rate of all units in the cofactor group is 0 and when the success rate is 1, respectively. The common cause failure model is calculated using the \(\beta\)-factor model. Normally, the value interval of the \(\beta\) factor is \([0.0-2.5]\). The larger the \(\beta\) factor is, the greater the impact of the common cause failure on the reliability of the system, and when \(\beta\) is 0, it means that there is no common cause failure occurs. When using the \(\beta\) factor model to calculate the common cause failure, the calculation formula is shown in Equation (28). \[C=\lambda\beta \tag{28}\]
## 1 Introduction
The difference between the azimuth estimate and the true azimuth is defined as the azimuth estimation error, and the difference between the elevation estimate and the true elevation is defined as the elevation estimation error. The cost function in the interferometer DOA estimation algorithm can be defined in several ways. In this paper, we adopt the cosine-based cost function [26]. The square error-based cost function can also be used [27]. Note that maximizing the cost function is required in the cosine-based cost function and that minimizing the cost function is required in the square error-based cost function  
By using a two-dimensional array, the azimuth and the elevation of an incident signal can be estimated simultaneously. Indeed, if the frequency hopping signal is wide-band, algorithms such as CSSM (coherent signal subspace method) should be used for DOA estimation [28; 29; 30; 31; 32]. The wide-band DOA estimation algorithm was introduced because the frequency of the array manifold differs from the carrier frequency of the signal source. Actually, the wide-band frequency of the signal source is focused and converted to manifold frequency in the CSSM algorithm. In practice, it is effective to use the narrow-band algorithm if possible, because it requires a large amount of computation to implement a wide-band direction-finding algorithm. In addition, the wide-band DOA algorithm requires an initial guess of the DOA estimate. Analysis of the azimuth error and the elevation error due to frequency mismatch is presented in this paper. From the viewpoint of the CSSM algorithm, this study examines how the performance of the DOA estimation algorithms degrades when the frequency of an incident signal is not exactly equal to the nominal frequency used for the calculation of the array manifold. In Section 2, we propose a method to derive the expressions of the azimuth estimation error and the elevation estimation error analytically in the presence of the frequency mismatch between the frequency of an incident signal and the nominal frequency used for the calculation of the array manifold. A summary of the derivation in Section 2 is presented in Section 3. In Section 4, the analytically-derived estimation errors are compared with the simulation-based estimation errors to validate the derived expressions of the azimuth and the elevation error. Section 5 concludes the paper
# IMPA versus Cloud Analysis and IDA: Different Methods to Evaluate Structural Seismic Fragility
## 3 Numerical Application  
### Frame Description  
The transverse frame modelled and analysed in this study comes from an actual school building in Norcia (Italy, 42.7941\({}^{\circ}\) North latitude, 13.0963\({}^{\circ}\) East longitude). The building, which originally consisted of three aligned blocks, was later joined into a single complex during the various refurbishment works. The building consists of a reinforced concrete (RC) frame structure with a footprint of 12.80 \(\times\) 59.80 m and a maximum height (from the foundations), corresponding to the roof beams, of about 16 m. The building consists of a one-floor basement, a ground floor, three storeys and an attic above ground. The inter-story height is 3.50 m for the basement floor and ground floor, 3.30 m for the other three floors, and 2.5 m for the attic (Figure 5). Built in 1962, the school has survived a variety of seismic events before the 6.5 magnitude central Italy earthquake that caused severe damage to structural and especially non-structural parts in 2016. In accordance with the construction methods of the time of construction, the structure was designed using 2D models schematizing the reinforced concrete frames in the transverse direction of the building. Although the legislation of the time did not explicitly require it, the designer also took into account the seismic action by applying an acceleration of 0.07 g.  
Figure 4: Flowchart schematically shows the main steps to develop seismic fragility curves for nonlinear static and dynamic procedures.  
Figure 5: _Cont_.  
The frame under study belongs to one of the two lateral blocks (Figure 5) and is a two-bay (5.65 m and 5.56 m span) regular cross frame. It is considered to be fixed at the base, and basement and soil-structure interaction has not been considered.  
The geometry of the frame, the column and beam geometry, and the reinforcement details are shown in Figures 5 and 6. The permanent structural load (G1) and permanent non-structural load (G2) are calculated as G1 + G2 = 5.1 kN/m\({}^{2}\) (from ground floor to 2 nd floor), G1 + G2 = 4.1 kN/m\({}^{2}\) (3 rd floor), G1 + G2 = 4.22 kN/m\({}^{2}\) (roof beams), the live load is taken as Q1 = 3 kN/m\({}^{2}\) (from ground floor to 2 nd floor), Q1 = 1 kN/m\({}^{2}\) (3 rd floor), Q2 = 1.8 kN/m\({}^{2}\) (roof beams) and taken as concentrated gravity on the columns at the edge of each floor. Each floor was assigned a seismic mass equal to 1/6 of the total mass of one of the three original blocks of the building.  
Fundamental and second periods of the frame have been evaluated as \(T_{1}\) = 0.62 s and \(T_{2}\) = 0.21 s, respectively.
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.6 Contiki  
Contiki [62] provides a core kernel, and supports loadable applications and services that can be dynamically replaced at runtime. These execute as processes, and share a single address space. The core is a single binary image, and typically consists of the kernel, the program loader, commonly used libraries, and a communication stack. It cannot generally be modified after deployment. Typically programs are downloaded into the system over the communication stack, and stored in EEPROM before being loaded/linked into the system using a relocatable binary format. When a service process is replaced, its process ID can be retained to support continuity of service; service state can also be transferred. Over-the-air programming is supported by a simple protocol that allows new binaries to be downloaded to selected concentrator nodes using point-to-point communication (over the communication stack). When the entire binary has been received, it is broadcast to neighboring nodes: these use negative acknowledgements to request retransmissions. Version numbers are used to match service interfaces calls with library functions. Version numbers are also used to support replaceable service processes, so that incompatible versions of a service will not try to load the stored service state.  
Contiki contains a number of features that might be of assistance in autonomous updates. The use of concentrator nodes might provide a useful mechanism for controlling updates, and getting feedback. Version number support is a key feature for autonomous updates, to avoid compatibility problems. As for other modular systems, the ability to reload modules might provide useful flexibility for autonomous updates.
## 5 Results and Discussion  
### TMPA and IMERG  
The separate accumulation of the TMPA (180-min temporal resolution and both 0.25\({}^{\circ}\) and 0.1\({}^{\circ}\) spatial resolutions) and IMERG (30-min temporal resolution and 0.1\({}^{\circ}\) spatial resolution) were used to generate the daily precipitation maps for each of the selected rainfall events shown in Figure 2. These spatial distributions illustrate the similarities and differences between the three resolution-based datasets. TRMM (0.25\({}^{\circ}\) and 0.1\({}^{\circ}\)) display very similar distributions. However, noticeable changes are noted between TMPA and IMERG, especially in the heavy-intensity 2016 event. In the following information, a comparison between the 0.1\({}^{\circ}\) resolution TMPA and IMERG data is performed and the results are shown in Table 2.  
The results of the Shapiro-Wilk normality test [61] have revealed that both datasets are non-normally distributed with \(p_{sat}\) < 0.05, at all times and for both the lowland and highland regions. This test was essential for determining the subsequent statistical analysis to be applied, as elaborated below. First, given that the data was determined to be non-normally distributed, the Wilcoxon signed-rank test [62] was applied in order to elucidate the similarities and differences between the two sets. For the 2015 lowland event, no significant differences between the two datasets were noted at the start of the event, but significant differences were noted later. Moreover, the two data sets pertaining to the highland region featured significant differences at all time thresholds of the precipitation event. For the 2016 event, a large difference was observed between the two datasets, in both the lowland and highland regions nearly every time. For the 2017 event, no significant differences were noted between the lowland datasets at time thresholds 0 h, 6 h, and 9 h. Significant differences were, however, apparent at the 3 h, 12 h, and 24 h time marks. Regarding the highland region, there were significant differences at 0 h, 6 h, 9 h, and 12 h, and no significant differences at 3 h and 24 h. The 2018 event featured highly significant differences between the two sets collected over the lowland region at 0 h, 6 h, and 9 h. However, no differences were recorded at 3 h, 12 h, and 24 h. The highland region is marked with no significant differences between the two datasets at 3 h and 6 h, but with highly significant differences at 0 h, 9 h, 12 h, and 24 h. Comparing the dataset differences during light-intensity events with those of the moderate-intensity to heavy-intensity events, it is clear that the data associated with light-intensity events generally features reduced variability and higher coherence. Comparing data from the lowland and highland regions, there was also a greater uniformity over the lowland region (Figure 3 and Table 2). Figure 2: Spatial distribution of rainfall over the area for each of the four events studied using TMPA and IMERG accumulated scenes (mm/d).