esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
As an example, the IGS MGEX network, consisting of more than 120 stations, was deployed in 2012 to facilitate the experiment with the emerging multi-system and multi-frequency data and to provide a well-established multi-GNSS service [14]. When compared with the single-system constellation, RT tropospheric products with enhanced availability, stability, and accuracy are anticipated from the fusion of multi-GNSS due to the increased number and the improved spatial geometry of the tracked satellites. The performance of RT tropospheric delays derived from multi-GNSS processing in the simulated RT mode has been investigated in some recent studies [8; 15]. For example, [PERSON] et al. performed a GPS + BDS combined solution for the RT water vapor retrieval and demonstrated that the accuracy of the derived water vapor could be improved from 1.7-2.1 mm to 1.5-1.8 mm when applying the combined instead of the GPS-only solution [8]. The RT water vapor retrieved from the four-system multi-GNSS combination (GPS + GLONASS + Galileo + BDS) was also studied by [PERSON] et al. [15]. The results showed that the water vapor estimates with higher accuracy (1.0-1.5 mm) and stronger reliability could be achieved from the multi-GNSS fusion when compared to the single-system solutions (e.g., GPS-only, GLONASS-only, and BDS-only). The latest investigation on multi-GNSS tropospheric delays retrieval in operational RT mode was carried out by [PERSON] et al. (2016). They extracted RT ZTD estimates based on GPS, GLONASS, and Galileo observations employing the Precise Point Positioning (PPP) technology. In their study, the Precise Point Positioning with Integer and Zero-difference Ambiguity Resolution Demonstrator (PPP-WIZARD) software offered by the Centre National d'Etudes Spatiales (CNES) ([PERSON] et al., 2017) was utilized, and the applied RT satellite orbit/clock products also were provided by from CNES. In this contribution, GPS, GLONASS, Galileo, and BDS observations are incorporated together for the operational RT tropospheric monitoring based on the PPP approach. The RT iPPP software ([PERSON] et al., 2017) is employed for data processing, and the satellites orbit/clock products provided by different IGS-RT analysis centers (e.g., BKG, GFZ) are utilized. Firstly, an experiment over 15 days is conducted to evaluate the performance of the RT satellites orbit/clock products from each IGS-RT service. The convergence time and the accuracy of RT ZTD estimates are assessed when applying different IGS-RT products. Then, the performance of RT ZTD retrieved from both single systems and from different multi-GNSS combination (concerning the weighting scheme for multi-system fusion, especially the contribution of BDS and Galileo) are investigated. Finally, the validation and comparison of different GNSS RT ZTD is carried out by using the IGS post-processing tropospheric products with high accuracy as reference.
## 3 Results
### Plasmid Construction Variations and Influence on clI Expression
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ClI-Construct Plasmids with Thermally** & **Intensity of** & **\(\lambda\)_immt34 \(\alpha\)I\({}^{-}\)** & **Cell Visibility (\(\pm\)SB) [\%: Plasmid Loss] Per** \\
**Inductive Expression of _clI_ Allele & **ClI-Activated Cl\({}_{434}\)** & **Plaque formation** & **Growth Temp. of Transformations** \\  & & **Expression of 37-39 \({}^{\circ}\)C\({}^{\ast}\)** & at 37-39 \({}^{\circ}\)C\({}^{\ast}\)** & **39 \({}^{\circ}\)C** & **42 \({}^{\circ}\)C** \\ \hline sR-38339-pE-clI (p747) & H & – & 0.62 (0.10) [0] & \(<\)0.001 (\(<\)0.004) [94] \\ sR-38339-pE-clI-loop-pO94 (p748) & 0 & + & 0.82 (0.02) [0] & 0.001 (\(<\)0.001) [94] \\ sR-3839-pE-clI-cy3048 (n= 10 \(pE\) [p767) & 0.8\({}^{\circ}\)b & + & 0.98 (0.01) [0] & 0.05 (\(<\)0.000) [9] \\ sR-38339-pE-clI-loop-p75 (p765) & 0.8\({}^{\circ}\)b & + & 0.69 (\(<\)0.001) [0] & 0.01 (\(<\)0.001) [0] \\ sR-38339-pE-clI-cy42 (n= 35 \(pE\) [p764) & 0.8\({}^{\circ}\)b & + & 1.00 (\(<\)0.020) [0] & 1.00 (\(<\)0.001) [42] \\ sR-38339-pE-clI-cy301 (n= 35 \(pE\) [p766) & H & – & 0.72 (\(<\)0.001) [0] & 0.001 (\(<\)0.001) [8] \\ cII-oop-pO4807 (p763) & H & – & 0.72 (\(>\)0.10) [0] & 0.07 (\(<\)0.001) [0] \\ cII-oop-pO45-3868.87 MHz (n= 10 \(p\) [p762) & H & – & 0.67 (0.10) [0] & \(<\)0.001 (\(<\)0.002) [100] \\ cII-oop-pO45-38683.87 MHz (n= 10 \(p\) [p762) & H & – & 0.78 (\(>\)0.20) [0] & \(<\)0.001 (\(<\)0.001) [50] \\ cII-oop-pO48638.87 MHz (n=10 \(p\) [p762) & H & – & 0.71 (\(<\)0.001) [0] & \(<\)0.001 (\(<\)0.0001) [56] \\ \hline \hline \end{tabular}  
* Designations as in Table 2. \({}^{\rm b}\) Q-S = Phase lysis spots/plaque areas were weak-turbid at 39 \({}^{\circ}\)C but well-formed clear plaques were observed at 37 \({}^{\circ}\)C. \({}^{\ast}\)Sequence change determined in putative \(-10\) region of \(pO\) recovered from p278\(pO\)\({}^{-}\)[39] was attafa to cgggCG and was identical to the synthetic change introduced into p759, see Figure S1, plasmids 2. \({}^{\rm d}\) Individual plaques were clear.  
\end{table} 
Table 5: Expression variations for _clI_ in 594 host. CII toxicity at 42 \({}^{\circ}\)C. The plasmid [sR-38,339-pE-cII] with added cy mutations 2001, 42, or 3048 poorly complemented in trans for CII (Table 5), permitting clear plaque formation by the infecting _imm_434_cII_ phages at 37 \({}^{\circ}\)C and weak- turbid pfu at 39 \({}^{\circ}\)C. (The cy3048 mutation was included in plasmids described in [47]). Plasmid [sR-38339-pE-cII] with cy3001 complemented better, suppressing plaque formation at 39 \({}^{\circ}\)C, yet supporting individual turbid plaque formation at 37 \({}^{\circ}\)C.  
It was assumed that neither the point mutations (cy3048, cy2001) introduced ahead of _cII_ in the \(-\)10 for _pE_ nor the silent mutations (cy42 and cy3001) within _cII_ in the \(-\)35 of _pE_ would influence CII complementation in trans. This assumption proved wrong, as CII complementation by [sR-38339-pE-cII] was altered by the putatively cis acting cy mutations.
# Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal
## 3 Related Theory
### Channel State Information  
Wi-Fi perception aims to realize human behaviour, object and environment perception in the propagation space by analyzing the wireless signal propagation channel characteristics. The propagation characteristic of this wireless signal is CSI. In other words, CSI is the compensation of various channel effects at the receiving end, such as signal reflection, refraction, diffraction and multi-path attenuation, i.e., Channel Impulse Response (CIR). The channel frequency response (CFR) can be obtained using the fast Fourier transform (FFT). CFR can provide amplitude and phase information of subcarrier level through Multiple Input Multiple Output (MIMO) and OFDM technology. According to Ref. [9], CFR can be expressed as:  
\[H(f,t)=\sum_{i=1}^{M}\alpha_{i}(f,t)e^{-j2\pi f\tau_{i}(f,t)} \tag{2}\]  
where \(\alpha_{i}(f,t)\) is the amplitude, \(\tau_{i}(f,t)\) is the phase information and \(M\) is the number of subcarriers.  
During actual communication, Wi-Fi signals are subject to interference from hardware devices, the environment, etc., and the received CSI data contains associated noise. So the accepted CSI structure is usually expressed as:  
\[H(f,t)=\left(\sum_{i=1}^{M}\alpha_{i}(f,t)e^{-j2\pi f\tau_{i}(f,t)}\right)e^{ -j(2\pi t\Delta f+\theta_{N}+\theta_{M})} \tag{3}\]  
Formula \(\Delta f\) represents the Central Frequency Offset (CFO) caused by the asynchronous clock between the transmitter and receiver; \(\theta_{N}\) is the phase offset caused by the sampling frequency offset; \(\theta_{M}\) is caused by hardware noise and the environment.
## 3 Proposed Methods
### Cooperative PSO-CS Algorithm  
The initialization matrix in PSO contains \(N\) particles dispersed in a \(D\)-dimensional search space, when in CS, this matrix is composed of \(N\) nests and each nest has \(D\) eggs (problem's dimension). In both PSO and CS, this primary population can be regarded of dimension \(D\times N\) and the quality of these solutions is evaluated in the same way. The global best particle in PSO (or the best nest in CS) is the particle (or the nest) that has the smallest fitness value among all potential solutions. Then, the velocities of displacement of particles and nests are calculated in different ways in PSO and CS. However, due to the fast convergence of PSO algorithm and the low convergence speed of the CS algorithm, the cooperation between PSO and CS would be very interesting to combine the ability of social thinking in PSO with the local search capability of CS. This cooperative PSO-CS aims to modify the speed equation of displacement by combining the Levy flights random walks of cuckoos and the movement of particles toward the global best solution in their vicinity \(Pg\). New solutions \(X_{i}\) (\(t+1\)) are given by:  
\[X_{i}(t+1)=X_{i}(t)+\alpha R\otimes Levy(\lambda)+C_{2}R_{2}\otimes(Pg(t)-X_{i }(t)) \tag{17}\]  
The use of cooperative PSO-CS helps to improve searching at global and local scales, which allows a balance between exploration and exploitation of the search space. Both algorithms are combined and involved to produce results. This combination of PSO and CS capabilities contribute to increase particle diversification in the entire search space and ensure good coverage during iterations. Thanks to this cooperation, CS algorithm can be proposed to control the quadrotor. Indeed, to evaluate the efficiency of the proposed PSO-CS method, comparisons with the intelligent approaches of PSO and CS are presented in the next sections for quadrotor's control and trajectory tracking.
## 4 Proposed Algorithm  
In the proposed algorithm, the network lifetime is divided into several rounds. The algorithm works in two phases, the setup phase and the steady-state phase, as shown in Figure 4. In the setup phase, the base station (BS) gathers the energy and location information of the deployed nodes, based on which it clusters the entire region into an optimal number of clusters (7 optimal clusters in our case), as shown in Figure 5. The BS, after deciding the optimal cluster ratio (\(C_{r}\)) in the network, performs the clustering using the k-means clustering algorithm [39]. Once the clusters are determined, the BS elects the CHs at or near the centroid of the clusters, which is followed by the steady-state phase.  
In the steady-state phase, when the cluster formation is accomplished, the non-cluster head sensing nodes sense the region and forward the sensed data to the appropriate CHs. The CHs aggregate the data, look for any redundancy and uncorrelated data and send it to the BS in a single hop. For better energy efficiency, the proposed algorithm elects the CHs near the centroid of the cluster and delays the subsequent setup phases, unlike in LEACH, until the residual energy of the current CH (\(CH\_E_{res}\)) >= specified threshold energy (\(Cluster\_E_{\textit{avg}}\)), thus conserving energy, as each setup phase consumes a significant amount of energy in exchanging the control packets required for the cluster formation.  
Algorithm 1 describes the proposed scheme, followed by Lemma 1\({}^{\prime}\)s analysis of its complexity. The clustering and cluster head selection processes used in the proposed algorithm are discussed in the following section.
## 2 Fundamental Concepts
### Wiretap Channel Models  
The secrecy problem of a physical layer system involves the transmission of information signals through the legitimate channel without conveying the information via the wiretap channel. The PLS achieves perfect secrecy by exploiting an advantage of a legitimate transmission channel in the presence of an eavesdropper. Therefore, the wiretap channel model is a significant representative design that examines the relationship between the physical layer secrecy and channel capacity within a wireless transmission system. In general, the wiretap channel model consists of MIMO channels, multiple-access channels, multi-user broadcast channels and other channels, such as relay channels, interference channels, etc. [39].
## 2 Multi-Parametric Monitoring of the Acute Test-Site
To this aim, the monitoring system was implemented through six on-rock mono-axial FBA11 accelerometers from KINEMETRICS (Pasadena, CA 91107, USA), three placed on the rock block and three on the rock wall, cable-connected to a KINEMETRICS K2 datalogger equipped with a tri-axial accelerometer placed at the slope foot and continuously acquired data a sampling rate of 250 Hz. The dynamic input was reproduced using a vibrodyne electro-mechanical excitation device, which is able to produce vibrations at a fixed frequency and amplitude in a range from 5 Hz to 25 Hz (70; 71) (Figure 2). A vibrodyne was located on a concrete base at the foot of the instrumented rock block and was operated to induce frequencies in a range between 5 Hz and 25 Hz with an incremental step of 5 Hz. After each shaking sequence of variable duration to transmit the same amount of energy to the slope system, the experiment was interrupted for at least one hour with the aim to restore undisturbed vibrational conditions within the rock wall. Table 2 summarises all the instrumental features of the devices composing the multisensor and multi-parametric monitoring system. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Device** & **Measuring Range** & **Precision** & **Repeatability** & **Resolution** & **Linearity** & **Natural Frequency** \\ \hline Air Thermometer & \(-\)40\(\pm\)60 \({}^{\circ}\)C & \(\pm\)0.1 K & \(\pm\)0.1 \({}^{\circ}\)C & - & - & - \\ Hygrometer & 0-100\% RH & \(\pm\)0.8\% RH & \(>\)5\% RH & - & - & - \\ Wind Speed & 1.5/79 m/s & \(\pm\)1.5 m/s & - & 0.1 m/s & - & - \\ Wind Direction & 0-352 & \(\pm\)7 & - & - & - & - \\ Rain Gage (_standard WMO_) & - & \(\pm\)0.2 mm & - & 0.2 mm H\({}_{2}\)O & - & - \\ Rock Thermometer & \(-\)30\(\pm\)100 \({}^{\circ}\)C & - & - & 0.1 \({}^{\circ}\)C & \(\pm\)0.15 \({}^{\circ}\)C & - \\ Strain Gage (1-LY41-50/120) & 50 mm (measurement base) & - & - & 1 \(\upmu\)strain & - & - \\ Extensometer & 25 mm (measurement base) & - & - & 0.01 mm & 0.5\% & - \\ Accelerometer (KINEMETRICS & \(\pm\)1 g & - & - & - & - & 50 Hz \\ FBA11 & \(\pm\)1 g & - & - & - & - & 50 Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Technical features of the installed devices. Figure 2: Devices installed at the Acuto test-site for the dynamic excitation experiments. The rock block is highlighted (**b**) with the disposition of the mono-axial accelerometers on the rock wall (black dashed circles) and on the rock block (red dashed circles). The mono-axial accelerometers installed on the ground (blue dashed circles) are shown in the main picture (**a**) with the vibrodyne device (**c**).
###### Abstract  
Using a novel technique, crosslinked gel polymer electrolytes (GPEs) designed for lithium-ion battery applications have been created. To form the photo crosslink via free-radical polymerization, a mixture of polyurethane acrylate (PUA), polyurethane methacrylate (PUMA), vinyl phosphoric acid (VPA), and bis[2-(methacryloloxy)ethyl] phosphate (BMEP) was exposed to ultraviolet (UV) radiation during the fabrication process. The unique crosslinked configuration of the membrane increased its stability and made it suitable for use with liquid electrolytes. The resulting GPE has a much higher ionic conductivity (1.83 \(\times\) 10\({}^{-3}\) S cm\({}^{-1}\)) than the commercially available Celgrad2500 separator. A crosslinked structure formed by the hydrophilic properties of the PUA-PUMA blend and the higher phosphate content from BMEP reduced the leakage of the electrolyte solution while at the same time providing a greater capacity for liquid retention, significantly improving the mechanical and thermal stability of the membrane. GPP2 shows electrochemical stability up to 3.78 V. The coin cell that was assembled with a LiFePO\({}_{4}\) cathode had remarkable cycling characteristics and generated a high reversible capacity of 149 mA h g\({}^{-1}\) at 0.1 C. It also managed to maintain a consistent Coulombic efficiency of almost 100%. Furthermore, 91.5% of the original discharge capacity was maintained. However, the improved ionic conductivity, superior electrochemical performance, and high safety of GPEs hold great promise for the development of flexible energy storage systems in the future.  
gel polymer electrolyte; UV-crosslinking; polyurethane acrylate; polyurethane methacrylate; free-radical polymerization; lithium-ion battery +
Footnote †: journal: Polymers
# Propulsion Performance of the Full-Active Flapping Foil in Time-Varying Freestream
###### Abstract  
A numerical investigation of the propulsion performance and hydrodynamic characters of the full-active flapping foil under time-varying freestream is conducted. The finite volume method is used to calculate the unsteady Reynolds averaged Navier-Stokes by commercial Computational Fluid Dynamics (CFD) software Fluent. A mesh of two-dimensional (2D) NACA0012 foil with the Reynolds number \(Re=\) 42,000 is used in all simulations. We first investigate the propulsion performance of the flapping foil in the parameter space of reduced frequency and pitching amplitude at a uniform flow velocity. We define the time-varying freestream as a superposition of steady flow and sinusoidal pulsating flow. Then, we study the influence of time-varying flow velocity on the propulsion performance of flapping foil and note that the influence of the time-varying flow is time dependent. For one period, we find that the oscillating amplitude and the oscillating frequency coefficient of the time-varying flow have a significant influence on the propulsion performance of the flapping foil. The influence of the time-varying flow is related to the motion parameters (reduced frequency and pitching amplitude) of the flapping foil. The larger the motion parameters, the more significant the impact of propulsion performance of the flapping foil. For multiple periods, we note that the time-varying freestream has little effect on the propulsion performance of the full-active flapping foil at different pitching amplitudes and reduced frequency. In summary, we conclude that the time-varying incoming flow has little effect on the flapping propulsion performance for multiple periods. We can simplify the time-varying flow to a steady flow field to a certain extent for numerical simulation.  
full-active flapping foil; time-varying freestream; propulsion performance; hydrodynamic character
## 3 Results and Discussion
### Vibration Displacement Analysis  
The signals acquired during machining were considered according to peak-to-peak and RMS indicators. The resulting parameter values for different toolholder types, depending on the change in technological parameters, are shown in Figures 5 and 6.  
Figure 4: (**a**) Vibration displacement and (**b**) vibration acceleration signals.  
Figure 3: Stability Lobe Diagram for different toolholders.  
Increasing the cutting speed led to a progressive growth in both peak-to-peak and RMS values, irrespective of which type of toolholder was applied. At the highest cutting speed \(v_{c}=1000\) m/min, a minor reduction in peak-to-peak values was also observed when using the shrinkfit and hydraulic toolholders. At the minimum cutting speed, the lowest vibration displacement occurred using the ER toolholder, while within \(v_{c}=600\)-\(1000\) m/min, the lowest values for both parameters were obtained using the hydraulic toolholder. Regardless of a change in cutting speed, the highest vibration displacement occurred when using the shrinkfit toolholder.  
When changing the feed per tooth, the trend in parameter changes was less conclusive. The peak-to-peak parameter values obtained when machining with the ER and hydraulic toolholders increased as a result of increasing the feed within \(f_{z}=0.05\)-\(0.15\) mm/tooth, while decreasing thereafter. In case of the shrinkfit toolholder, the decrease in values only occurred at the highest feed. The lowest peak-to-peak values were achieved in most cases using the ER toolholder. For the RMS parameter, the trend in changes due to a varying feed per tooth was inconclusive, irrespective of the toolholder used. Within \(f_{z}=0.05\)-\(0.20\) mm/tooth, indicator values were similar for all toolholders, while for the highest feed values, the ER toolholder proved to be the most beneficial one.  
Comparing the obtained results with other studies carried out on magnesium alloys showed that higher vibration displacement values were obtained during trochoidal milling. The differences were not relatively large, but machining along straight paths proved more advantageous.  
Figure 5: (**a**) Peak-to-peak and (**b**) RMS values for vibration displacement with different cutting speeds.  
Figure 6: (**a**) Peak-to-peak and (**b**) RMS values for vibration displacement with different feed per tooth.
# Evaluation of Antioxidant and Anticorrosive Activities of _Ceriops_ _tagal_ Plant Extract
## 4 Conclusions  
* This study mainly focuses on emphasizing the eminent role of the incredible mangrove plant _C. tagal_ in preventing oxidation and corrosion.
* The amount of TPC and TFC highlights the need to focus on other active ingredient in _C. tagal_ to learn more about the plant in the future.
* The scavenging activity on DPPH, nitric oxide, and hydrogen peroxide portraits the performance of _C. tagal_ as a successful antioxidant.
* The inhibition efficiency of the plants is more evident in weight loss, ASS, UV-visible, SEM-EDX, and AFM for corrosion inhibition.
* Although there is no direct correlation between the antioxidant and anticorrosive activities, similar studies on several other plants and their components have highlighted the role of secondary metabolites in both cases. Thus, the _C. tagal_ plant can be taken for further research on metal inhibition and other pharmaceutical applications.  
Conceptualization, methodology, data curation, writing--original draft preparation, M.S.; conceptualization, resources, visualization, writing--original draft preparation, K.K.; conceptualization, software, investigation, data curation, writing--original draft preparation, writing--review and editing, supervision, project administration, M.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This paper was supported by the KU Research Professor Program of Konkuk University.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline
**S. No** & **Green Inhibitor** & \(\boldsymbol{\eta\) (\%)} \\ \hline
1. & _Aster koraiensis_ & 90.53 [23] \\
2. & _Ceriops tagal_ \# & 95 \\
3. & _Cryptostegia grandiflora_ & 87.54 [25] \\
4. & Sweet melon peel & 91.59 [30] \\
5. & _Desmodium triflorum_ & 92.99 [32] \\
6. & _Polycarpaa corymbosa_ & 91.78 [32] \\
7. & _Citrullus lanatus_ & 91.00 [57] \\
8. & _Lepidagathis keralensis_ & 92.06 [72] \\
9. & _Mangifera indica_ & 92.73 [73] \\
10. & _Mangifera indica_ & 92.00 [74] \\
11. & _Mentha pulegium_ & 80.00 [75] \\
\# Present work.  
\end{table}
Table 5: Comparison of _C. tagal_ (present) green inhibitor over other green inhibitors.