esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Optimization of FtsZ Expression  
To obtain a high expression level of FtsZ protein, positive transformants were selected to cultivate in LB medium to an OD\({}_{600}\) of 0.6-0.8. Cultures were then induced by different concentrations of IPTG (0.2, 0.4, and 0.6 mM) at different temperatures (16, 25, and 37 \({}^{\circ}\)C) for different time periods (1, 3, 5, and 7 h). The results showed that the expression level of FtsZ at 37 \({}^{\circ}\)C was significantly higher than those at 16 \({}^{\circ}\)C and 25 \({}^{\circ}\)C (Figure 1C). Moreover, after induction at 37 \({}^{\circ}\)C for 5 h, the FtsZ level was higher than at other time periods (including 1 h, 3 h, and 7 h) (Figure 1D); after induction with 0.2 mM IPTG for 5 h, a higher level of FtsZ protein was observed than with other IPTG concentrations (Figure 1D). Overall, the highest expression level of FtsZ protein was induced by 0.2 mM IPTG at 37 \({}^{\circ}\)C for 5 h (accounting for approximately 50% of total cell proteins).  
The molecular weight (MW) of the recombinant FtsZ protein was approximately 41 kDa (Figure 1D), which is consistent with the theoretical value of 41.13 kDa, indicating that the FtsZ protein was successfully produced in _E. coli_. Additionally, FtsZ protein was expressed as inclusion bodies in _E. coli_ BL21 (Figure 2).  
Figure 1: Construction of recombinant plasmids and FtsZ protein expressed in _E. coli_ under different conditions. **(A)** Construction of recombinant plasmids. **(B)** Digestion of recombinant plasmids with endonucleases. Lane 1: digestion of pMD-ftsZ plasmids with _Nco_ I and _Xho_ I; lane 2: digestion of pET-28a-ftsZ plasmids with _Nco_ I and _Xho_ I. M: marker. **(C)** Expression of FtsZ at different temperatures. M: marker. **(D)** Different induction times (I, 3, 5, and 7 h) and IPTG concentrations ranging from 0.2 to 0.6 mM. PC: bovine serum albumin; M: marker.
## 2 Results and Discussion
### Effects of HPP and TS Processing on Quality Properties during Different Storage Conditions  
#### 2.2.1 Texture Changes  
Softening of fruits and vegetables is a process of destruction of the cell structure caused by the breaking of cell walls and dissolution of pectin by the actions of enzymes. During storage, cell rupture due to temperature changes, microbial growth, and a series of biochemical reactions eventually lead to a decrease in product hardness. Therefore, the hardness of fruits and vegetables is often used as an important indicator of quality changes during storage [36; 37]. As shown in Figure 2, the hardness of marinated lotus root slices tended to decrease at the three temperatures during storage and finally reached a value of approximately 5000 g at the end of the shelf life. The hardness values of the marinated lotus root slices decreased faster at higher storage temperatures. These findings were consistent with those of previous studies on atemoya [38]. The most important softening process in the HPP or TS process is considered to be the temperature-dependent \(\beta\)-elimination of pectin [39]. In addition, most of the hardness values of HPP marinated lotus root slices were significantly higher (\(p<0.05\)) than those of TS marinated lotus root slices stored at the same temperature (Figure 2). This finding was consistent with the study by [PERSON] et al. [40]  
Figure 1: The appearance of blank (**left**), HPP (**middle**), and TS (**right**) marinated lotus root slices. HPP, high-pressure processing; TS, thermal-and-soaking.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Blank** & **HPP** & **TS** \\ \hline TVC (log\({}_{10}\) CFU/g) & 3.98 \(\pm\) 0.04 a & 1.68 \(\pm\) 0.02 b & 1.60 \(\pm\) 0.16 b \\ Hardness (g) & 14,208.14 \(\pm\) 1985.80 a & 14,820.45 \(\pm\) 814.85 a & 11,955.97 \(\pm\) 1040.45 b \\ pH & 6.47 \(\pm\) 0.02 a & 4.82 \(\pm\) 0.03 b & 4.73 \(\pm\) 0.04 c \\ L* & 76.35 \(\pm\) 14.2 a & 51.74 \(\pm\) 1.52 b & 48.36 \(\pm\) 0.68 c \\  & a* & \(-\)0.88 \(\pm\) 0.05 c & 7.58 \(\pm\) 0.57 b & 8.48 \(\pm\) 0.43 a \\ Color & b* & 2.65 \(\pm\) 0.32 c & 21.56 \(\pm\) 1.20 b & 23.95 \(\pm\) 0.74 a \\  & \(\Delta\)E & - & 32.20 \(\pm\) 0.45 b & 36.41 \(\pm\) 0.39 a \\ \hline \hline \end{tabular} Different lowercase letters in the same row indicate significant differences among the treatments (\(p<0.05\)). HPP, high-pressure processing; TS, thermal-and-soaking; TVC, total viable count; \(\Delta\)E, total color difference.  
\end{table}
Table 2: Quality properties of HPP and TS marinated lotus root slices.  
on yellow peaches in pouches, indicating that HPP could maintain the texture of food. This is due to the release of PME under HPP treatment, which catalyzed the demethylation of high-methylated pectin, and the resulting de-esterified pectin (low-methylated pectin) forms a gel network with divalent ions [41].
# Online SOPDT Model Identification Method Using a Relay
## 2 Description of the Method
### Determination of the Steady-State Gain  
With the help of the relay, not only are the ultimate oscillations generated but the steady-state gain \(K\) and the dead time \(\theta\) can also be determined. At the start of the identification test, the relay is connected in parallel to the controller so that the control signal from this moment will be the sum of both outputs. It should be noted that the controller output will be \"frozen\" at the level of the start of the test. To ensure that the relay does not switch after the change of the error sign, the hysteresis zone \(\varepsilon\) should have a value higher than an increase of the controlled variable in reaction to a signal from the relay, i.e.,  
\[\varepsilon>K_{e}\cdot B \tag{3}\]  
where \(K_{e}\) is the static gain, which can be estimated from \(K_{e}\cong y_{st}/u_{st}\); \(y_{st}\) and \(u_{st}\) are, respectively, the controlled variable and control signals in the steady state.  
After switching the relay from level 0 to \(B\), the control signal will increase to the value \(\mathrm{u}(t_{\mathrm{t}})+B\), leading to an increment in the controlled variable equal to \(\Delta y\):  
\[\Delta y=K\cdot B \tag{4}\]  
The measurement of this steady-state increment allows for the static gain of the plant to be determined:  
\[K=\Delta y/B \tag{5}\]  
After the steady-state gain is determined, the hysteresis is reduced, thus changing the state of the relay output from \(B\) to 0. This allows for the measurement of the dead time, which is equal to the time from switching the relay from the high to low state to the process response to this change. This is illustrated in Figure 1.  
During the identification, the output signal from the PID controller remains unchanged and does not react to any changes in the error signal.  
The method presented in this section for determining the steady-state gain is not the only possible one. Using an asymmetric relay, the gain can be determined as the ratio of the integral from the input signal to the integral from the plant's input signal [13].
# Effect of Heat Treatment on Some Titanium Alloys Used as Biomaterials
###### Abstract  
Titanium-based alloys are constantly improved to obtain properties suitable for their use. Improving titanium alloys is very important for performing alloys without side effects. In this paper effects of structure, microhardness, and indentation test of eight titanium alloys were investigated after aging. The heat treatment consisted of a high-temperature quenching accomplished in three steps (650 \({}^{\circ}\)C for 25 min, 850 \({}^{\circ}\)C for 20 min, and 950 \({}^{\circ}\)C for 20 min). The cooling process was accomplished using N\({}_{2}\) gas, introduced in the chamber at a 9-bar pressure for 37 min. Then, followed by heating to a constant temperature tempering (550 \({}^{\circ}\)C) at 1.5 bar pressure and kept for 2 h and 10 min at 2 bar pressure. Optical microscopy images were obtained of Ti-Mo-Zr-Ta alloys with grain-specific aspects of titanium alloys; acicular and coarse structures are specific to \(\beta\) alloys. Microhardness results showed significantly influenced by the heat treatment, increased by approximately 5% for Ti15 Mo/Zr15 Ta1 Si and Ti20 Mo/Zr15 Ta0.5 Si, while for Ti15 Mo/Zr15 Ta0.5 Si and Ti20 Mo/Zr15 Ta an approximately 9% decrease has been noted. The modulus of elasticity results obtained by the indentation method for the experimental alloys were between 36.25-66.24 GPa. The heat treatments applied to the alloys had a pronounced effect, improving both the structure of the alloys and the results of the indentation test.  
biomaterials; titanium-alloys; heat-treatment; microscopy; microhardness
# Design and Diagnostics of High-Precision Accelerator Neutrino Beams
## 2 Accelerator Neutrino Beams
### Non-Conventional Neutrino Beams  
Several \"non-conventional\" neutrino beam concepts have been developed over the years. In these cases, the neutrinos are produced by leptons or by the \(\beta\)-decays of partially ionized isotopes. The proposed _Neutrino Factories_[17] would create, accelerate and store in a ring charged muons, exploiting the \(\mu^{\pm}\to e^{\pm}+\
u_{e}(\bar{\
u}_{e})+\bar{\
u}_{\mu}(\
u_{\mu})\) decay channel. Here, the beam composition is precisely known to be 50% \(\
u_{e}(\bar{\
u}_{e})\) and 50% \(\bar{\
u}_{\mu}(\
u_{\mu})\). If a Neutrino Factory were built, it would offer major advantages compared with conventional beams. Firstly, the number and polarization of the muons can be easily measured in the storage ring, offering superior control of flux and beam composition. Furthermore, the beam would be free of contamination because the neutrino flavors are uniquely determined by the muon decay products. Finally, a Neutrino Factory could be a powerful source of \(\
u_{e}\) and \(\overline{\
u}_{e}\), unlike conventional beams where \(\
u_{e}\) and \(\overline{\
u}_{e}\) are always sub-dominant. A systematic review of the physics of Neutrino Factories is available in [18; 19]. However, the technical challenges of a Neutrino Factory are tremendous. Due to the finite lifetime, the muons must be accelerated very quickly and the transverse momentum \(p_{T}\) must be highly reduced (_muon cooling_) before injection into the storage ring [17; 20; 21].  
Neutrinos can also be produced by beta decays of accelerated isotopes and generate a single-flavor beam (\(\bar{\
u}_{e}\) or \(\
u_{e}\) for \(\beta^{-}\) or \(\beta^{+}\) decays, respectively) with excellent control of the flux. The neutrino energy spectrum depends on the accelerator that boosts the isotope and the three-body kinematics of the \(\beta\) decays. This facility, known as _Beta Beam_, has been proposed in 2002 [22] but was never implemented despite a significant R&D. Again, the technical challenges of creating, selecting and accelerating beta-unstable isotopes with high-rigidity have not been overcome, yet [23].
## 5 Proposed NSGA-II Algorithm with Adaptive Local Search
### Adaptive Local Search Operator  
To improve the performance of our algorithm we used an adaptive local search method based on a simulated annealing algorithm. Similar to [PERSON] et al. [8], the method is also based on the concept of similarity. When the genetic algorithm is continuously converging, the similarity among the individuals of a genetic algorithm population increases. Therefore, the fitness values of the individuals are similar to each other to a significant extent and the heterogeneity of the population is reduced [8]. The local search procedure will be invoked in a genetic algorithm loop when the similarity of the population exceeds a pre-defined threshold value (\(\beta\)), otherwise only genetic algorithm is applied.  
Compared with applying a local search in every iteration, the adaptive local search method takes much less time and produces high quality results. The similarity coefficient \(SC_{pq}\) in our paper is calculated as follows:  
\[SC_{pq}=\frac{\sum_{k=1}^{n}\varphi\Big{(}f_{pk},\ f_{qk}\Big{)}}{n} \tag{24}\]  
where \(f_{pk}\) and \(f_{qk}\) are the facilities at location \(k\) in chromosomes \(p\) and \(q\), \(k\) is the index of location in the layout, and \(n\) is the number of facilities. The calculation of \(\varphi\Big{(}f_{pk},\ f_{qk}\Big{)}\) is as follows:  
\[\varphi\Big{(}f_{pk},\ f_{qk}\Big{)}=\left\{\begin{array}{cc}1&if\ \Big{(}f_{pk}=\ f_{qk}\Big{)}\\ 0&\text{otherwise}\end{array}\right. \tag{25}\]  
\begin{table}
\begin{tabular}{c c The average similarity coefficient \(\overline{SC}\) of the current population can be obtained by Equation (26) as follows:  
\[\overline{SC}=\frac{2\sum_{p=1}^{P_{S}-1}\sum_{q=p+1}^{P_{S}}SC_{pq}}{Ps*(Ps-1)} \tag{26}\]  
where \(Ps\) refers to the size of population. When \(\overline{SC}\) exceeds the threshold \(\beta\), the local search procedure is applied to some individuals which are randomly selected from the population.  
The process of the local search is described as follows. At first, all the relevant parameters are initialized. A new individual will be generated by the mutation methods in Section 5.5 and compared to the current individual. If the new individual dominates the current solution, the current individual will be replaced by the new individual. Otherwise, the current individual will be replaced by an acceptance probability calculated as Equation (27):  
\[P_{acceptance}=\prod_{i=1}^{2}\exp(-\frac{|f_{i}(X_{2})-f_{i}(X_{1})|}{T}) \tag{27}\]  
where the \(T\) is the current temperature and \(f_{i}(X_{2})\) and \(f_{i}(X_{1})\) are the two objective function values of the new individual and the current individual, respectively. This process is repeated for _loop_inner_ iterations, then the temperature is lowered by multiplying the cooling rate \(\alpha\). The above process will be repeats until the temperature is lower than the final temperature \(T\_final\).
# Indoor Positioning by Double Deep Q-Network in VLC-Based Empty Office Environment
## 6 Conclusions  
Recently, due to the increasing complexity and size of indoor environments, the LBS industry has experienced rapid growth. Achieving precise positioning is crucial for delivering high-quality LBSs. Hence, this paper focuses on applying an AI algorithm based on a double deep Q-Network model for user positioning in VLC-based indoor environments. The proposed method combines the existing fingerprinting technique with the double deep Q-Network model. The main components of this study include analyzing the channel characteristics of the indoor VLC environment, constructing a fingerprinting map, and applying the double deep Q-Network model. This paper emphasizes the optimal matching based on the fast processing time of the double deep Q-Network model. For this purpose, each element of reinforcement learning is defined and mathematical modeling  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Performance**} & **FP-Based DDQN** & \multirow{2}{*}{**KNN**} & \multirow{2}{*}{**Triangulation**} \\  & **(Proposed)** & & \\ \hline Positioning & & & \\ Error [m] & 0.13 & 0.191 & 0.3281 \\ Processing & & & \\ Time [s] & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between proposed and conventional scheme.  
Figure 7: Training performance of double deep Q-Network model.  
is performed. The double deep Q-Network model is trained through simulations for approximately 100 episodes. By utilizing the trained positioning model, it is shown that it achieves a positioning accuracy of 13 cm and just 0.03 s of time duration.  
In the future, there are plans to explore additional deep reinforcement learning models, such as dueling DQN and the deep deterministic policy gradient (DDPG). Although the processing time in the online step of the fingerprinting was reduced in this study, the issue of the cost of constructing the database in the offline step still remains. Furthermore, it is also considered to enhance the learning process by incorporating semi-supervised reinforcement learning, which utilizes a small amount of data for training.  
Conceptualization, S.H.O. and J.G.K.; methodology, S.H.O. and J.G.K.; software (MATLAB 2017b, Python 3.7, Keras-rl2), S.H.O.; validation, S.H.O. and J.G.K.; formal analysis, S.H.O. and J.G.K.; investigation, S.H.O.; resources, J.G.K.; data curation, S.H.O.; writing--original draft preparation, S.H.O.; writing--review and editing, J.G.K.; visualization, S.H.O.; supervision, J.G.K.; project administration, J.G.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2021R1F1A1063845).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## Materials and Methods
### Data
#### 2.2.3 CryoSat-2 Radar Altimeter and IceBridge ATM Laser Altimeter  
The collected DEMs for the two-dimensional accuracy assessment was out-of-date compared to the TanDEM-X observations applied in this study. Therefore, we collected altimeter data, which was acquired in the same period when the TanDEM-X observed, to validate further on the constructed DEM. CryoSat-2 is a radar altimetry satellite built by the European Space Agency (ESA) and its mission is dedicated to the monitoring of ice sheets on land and sea ice in the ocean. The orbit of CryoSat-2 has a 92\({}^{\circ}\) inclination and a 717-km altitude and covers nearly all of the polar regions (\(\sim\) 88\({}^{\circ}\)N). The Synthetic Aperture Radar/Interferometric Radar Altimeter (SIRAL) altimeter in the Ku-band (13.575 GHz) provides an approximately 0.3 km by 1.5 km area along track and across track, which is a greatly reduced size of footprint compared to previous ESA altimeter missions, such as ERS and the Envisat system (\(\sim\) 10 km) [41; 42]. Three operational modes of low resolution, SAR, and SARIn are available. The height accuracy of CryoSat-2 SAR has more than 4 m of bias at the steep margin of the ice sheet, and a 1.5-m bias in flat areas with slopes less than 0.2\({}^{\circ}\)[43; 44]. A total of 22 CryoSat-2 SARIn mode observations from January 22, 2015 to March 21, 2015 were collected to validate the constructed DEM and convert the geodetic map projection into a polar stereographic projection (Table 2).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **TerraSAR-X/TanDEM-X** \\ \hline Acquisition date & February 21, 2015 \\ Carrier frequency & X-band (9.6 GHz) \\ Wavelength & 3.1 cm \\ Polarization & HH \\ Incidence angle & 35.28/35.33 degrees \\ Pulse repetition frequency & 3700 Hz \\ ADC sampling rate & 164.8 MHz \\ Azimuth pixel spacing & 1.90 m \\ Range pixel spacing & 0.91 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: TerraSAR-X and TanDEM-X SAR data characteristics used in this study.  
The ATM is a laser altimeter cooperating with a scanning LiDAR at 5 kHz developed by the National Aeronautics and Space Administration (NASA) to observe the Earth's topography for the measurement of changing glaciers in the Arctic and Antarctic as part of NASA's Operation IceBridge, which began in 2009. The nominal survey altitude was between 500 and 750 m above the ice surface and the swath width was approximately 400 m. The ATM by incorporating measurements from GPS receivers and Inertial Navigation System (INS) attitude sensors, provides topography with an accuracy of approximately 6.6 to 8.5 cm depending on the size of the area [45; 46]. The ATM measurements collected from the six flight paths on May 5, 2015 were obtained to verify our results [47]. They were also converted into a polar stereographic projection compared to that of the other observations.
## 6 Numerical Results
### Network Life-Cycle  
Network life-cycle is an important parameter in the area of WSNs to identify the robustness of a proposed approach by studying the number of alive sensor-nodes during the network's operation. Figure 8 depicts the comparison of FRM with CBS in case of network life-cycle that proves, generally, our approach can last longer and it balances the energy consumption among the sensor-nodes which protects the network from a dramatic death.  
The main reason for the phenomenon of dramatic death in case of assuming a fixed trajectory in the environment is related to the hot-spot phenomenon. In fact, the sensor-nodes which are in the periphery of the trajectory due to their duties in the network consume a higher amount of energy in contrast with the sensor-nodes which are located far away from the trajectory. Therefore, in this scenario it is important to design an approach to avoid this phenomenon. And our proposed approach can reach to this goal by defining layers of duties from the sensor-nodes and preventing from unnecessary communication among the sensor-nodes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **WSN#1** & **WSN#2** & **WSN#3** & **WSN#4** \\ \hline Area (L \(\times\) L) & 100 \(\times\) 100 m\({}^{2}\) & 100 \(\times\) 100 m\({}^{2}\) & 200 \(\times\) 200 m\({}^{2}\) & 200 \(\times\) 200 m\({}^{2}\) \\ Number of sensor-nodes & 100 & 100 & 200 & 200 \\ Initial energy of the sensor-nodes & 0.5 J & 0.5 J & 0.5 J & 0.5 J \\ Communication radius of sensor-nodes & 10 m & 10 m & 20 m & 20 m \\ \(E_{elect}\) & 50 n/l/bit & 50 n/l/bit & 50 n/l/bit & 50 n/l/bit \\ \(\xi_{fs}\) & 10 p/l/bit/m\({}^{2}\) & 10 p/bit/m\({}^{2}\) & 10 p/l/bit/m\({}^{2}\) & 10 p/l/bit/m\({}^{2}\) \\ \(\xi_{mp}\) & 0.0013 p/bit/m\({}^{4}\) & 0.0013 p/bit/m\({}^{4}\) & 0.0013 p/bit/m\({}^{2}\) & 0.0013 p/bit/m\({}^{2}\) \\ Energy for data aggregation (\(E_{DA}\)) & 5 n/l/bit & 5 n/l/bit & 5 n/l/bit \\ Aggregation rate & 0.6 & 0.8 & 0.6 & 0.8 \\ Control packet & 200 bits & 200 bits & 200 bits & 200 bits \\ Message packet & 4000 bits & 4000 bits & 4000 bits & 4000 bits \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental parameter settings [39; 54].  
Figure 8: Comparison in case of network life-cycle (**a**) WSN#1; (**b**) WSN#2; (**c**) WSN#3; and (**d**) WSN#4.
## 9 Effect of Mesh Quality on the Algorithm Performance and Results of Simulations
Namely, in the assessment of the mesh quality the common view is that the dihedral angle between the tetrahedron faces is a significant parameter determining the mesh quality\(-\)for the ideal mesh the measure of the dihedral angle between any faces should be 70.53\({}^{\circ}\). Therefore, the Stellar package is oriented mainly towards improving that parameter The simulations presented in this paper indicate, however, that at least for the diffusive-reactive model the improvement of the parameter connected with the quality of dihedral is not of a great importance\(-\)compare the results for Mesh 2 and Mesh 3 in Table 11. Apart from the fact that after applying Stellar package the dihedral angle parameter of the Mesh 3 is significantly better than that of Mesh 2\(-\)Table 9\(-\)the results for the both meshes are practically the same.