esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Metalloprotease from _Dunaliella_ sp.: Production, Regulation, and Structural Insight
## 4 Conclusions  
An isolated microalgal strain from a poorly exploited Tunisian biotope was identified as belonging to the _Dunaliella_ genus, based on both morphological characteristics and 18S rRNA gene sequencing. The impact of diverse cultivation conditions on protein and protease production in _Dunaliella_ sp. was investigated. Optimal specific proteolytic activities were observed when _the stain_ thrived in a modified F/2 medium containing 45.54 g/L NaCl and 1.5 g/L NaHCO\({}_{3}\), at a temperature of 28 \({}^{\circ}\)C and under a light intensity of 100 \(\upmu\)mol photons/m\({}^{2}\)/s. Insight into ATP-dependent metalloprotease (FtsH2) expression showed its upregulation in response to salt stress, GA3, and IBA. Finally, structural analysis demonstrated two possible types of arrangement (i) a perfect six-fold symmetry arrangement with an open circular entrance and (ii) a two-fold symmetry model induced by substrate binding to the aromatic pore residues.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app142210402/s1](https://www.mdpi.com/article/10.3390/app142210402/s1)]([https://www.mdpi.com/article/10.3390/app142210402/s1](https://www.mdpi.com/article/10.3390/app142210402/s1)).  
**Author Contributions:** Conceptualization, S.A., P.M. and H.B.H.; methodology, F.E. and M.D.; software, O.H., H.B.H., M.D. and L.T.; validation, S.A., M.D. and H.B.H.; formal analysis, O.H., F.E. and L.T.; investigation, O.H., L.T., M.D., H.B.H. and F.E.; resources, S.A.; data curation, O.H.; writing--original draft preparation, O.H.; writing--review and editing, P.M. and S.A.; visualization, O.H. and P.M.; supervision, S.A.; project administration, S.A. and P.M., funding acquisition, S.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
Figure 6: Surface presentation of Apo-FtsH2 (**A,C**) and ADP-bound FtsH2 (**B,D**), where the aromatic residue PHE 158 is highlighted as a red surface (monomers are showed with different colors). Slabbed electrostatic surface presentation of Apo-FtsH2 (**E**) and ADP-bound FtsH2 (**F**) colored red and blue for negative and positive charges, respectively, and white color represents neutral residues.
# Low-Carbon Operation Strategy of Park-Level Integrated Energy System with Firefly Algorithm
## 4 Operational Strategy Optimization Model
### Operating Costs  
Operational costs are the sum of energy procurement costs and equipment maintenance costs, and can be represented by the following equation:  
\[F_{eco}=C_{op}+C_{en} \tag{12}\]  
In this equation, \(F_{eco}\) represents operational costs, yuan, and \(C_{op}\) denotes equipment maintenance costs, yuan. Ideally, the maintenance costs of each device are directly proportional to its output power and operating time. \(C_{en}\) stands for energy procurement costs, yuan. Both can be expressed by the following formulas, respectively:\[C_{op}=\sum_{t=1}^{T}{[\lambda_{WT}P_{WT}(t)+\lambda_{PV}P_{PV}(t)+\lambda_{GT}P_{ GT}(t)+\lambda_{GB}P_{GB}(t)+\lambda_{EC}P_{EC}(t)+\lambda_{RC}P_{RC}(t)+ \lambda_{ES}P_{ES}(t)+\lambda_{TS}P_{TS}(t)]\Delta t} \tag{13}\]  
In this equation, \(P_{X}(t)\) denotes the average output power of device X during the time interval \(\Delta t\) from time \(t-1\) to \(t\), kW, and \(\lambda_{X}\) represents the operational and maintenance cost coefficient of device X. The subscripts of the variables have been explained previously in the text.  
\[C_{en}=\delta_{gas}{\sum_{t=1}^{T}{[F_{GT}(t)+F_{GB}(t)]\Delta t+\delta_{el}{ \sum_{t=1}^{T}{P_{grid}(t)\Delta t}}}} \tag{14}\]  
In this equation, \(\delta_{gas}\) and \(\delta_{el}\) respectively represent the prices of natural gas and electricity, yuan/kWh. \(F_{GT}(t)\), \(F_{GB}(t)\), and \(P_{grid}(t)\) respectively denote the average combustion rates of natural gas in a micro gas turbine, the average combustion rate of natural gas in a gas boiler, and the average power supplied by the electrical grid during the \(\Delta t\) time interval from time \(t-1\) to \(t\), kW.
# Development of Automatic Berthing Support Program for Autonomous Ships
## 2 Methods  
### Forces Acting on the Hull During Berthing  
When a ship berths at a pier, external forces such as wind and current can cause hull drift or rotation. These effects are primarily due to wind pressure acting on the upper surfaces and fluid forces on the lower surfaces, resulting from the current and lateral movement of the ship. These forces generate moments that rotate the bow relative to the hull's center of gravity.
## 3 Results  
### Drag Force versus Projected Frontal Area  
Aerodynamic drag force from the CFD simulations of the 3D models of all 10 participants is shown in Table 3. Using these and the corresponding projected frontal area, drag coefficients C\({}_{\text{D}}\) can be obtained from Equation (1) as air density and wind velocity are known and are constant (Table 2). These C\({}_{\text{D}}\) values are also listed in Table 3. Drag force is plotted with projected frontal area in Figure 7. This area was compared with area from the depth camera and the comparison per pose is shown in Figure 8. The regression analysis of drag and corresponding area for all poses (3 poses \(\times\) 10 participants = 30 data points) yielded a coefficient of determination of R\({}^{2}\) of 0.72.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **Participant** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \multirow{2}{*}{_TT_} & Drag (N) & 28.79 & 26.81 & 27.49 & 26.94 & 26.84 & 29.59 & 31.30 & 28.71 & 29.86 & 35.71 \\  & C\({}_{\text{D}}\) & 0.70 & 0.58 & 0.64 & 0.63 & 0.69 & 0.72 & 0.69 & 0.63 & 0.76 & 0.76 \\  & Drag (N) & 37.43 & 35.99 & 37.31 & 44.23 & 31.80 & 39.07 & 45.03 & 36.13 & 35.97 & 41.67 \\  & C\({}_{\text{D}}\) & 0.74 & 0.66 & 0.70 & 0.81 & 0.70 & 0.79 & 0.78 & 0.66 & 0.73 & 0.75 \\  & Drag (N) & 32.29 & 33.15 & 32.40 & 32.56 & 30.07 & 33.99 & 35.08 & 31.32 & 25.99 & 40.01 \\  & C\({}_{\text{D}}\) & 0.66 & 0.66 & 0.69 & 0.65 & 0.72 & 0.76 & 0.69 & 0.64 & 0.63 & 0.76 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Drag force of the 3D models from the Static protocol. C\({}_{\text{D}}\) was obtained by using Equation (1) and the area from the projected frontal area camera.
# The Potential of _Phaeodactylum_ as a Natural Source of Antioxidants for Fish Oil Stabilization
## 3 Results and Discussion
### _Phaeodactylum_ Total Lipid Extract-Fish Oil Mixtures (Part II)
#### 3.2.2 Primary Oxidation  
Figure 4 shows the primary oxidation results of the six different systems. A clear separation in \(\Delta\)PV progression between 'Pure _Phaeodactylum_ total lipid extract', 'Mixture 79/21' and 'Mixture 49/51' on the one hand, and 'Mixture 6/94', 'Mixture 1/99' and 'Pure Fish oil' on the other hand was observed. The first three showed a rather small, insignificant increase in \(\Delta\)PV during storage, indicating that primary oxidation was only minor or absent. After 10 weeks, increases of 52 (\(\pm\)33), 19 (\(\pm\)4), and 21 (\(\pm\)13) meq hydroperoxides/kg lipids were obtained, respectively. For the last three systems, much higher \(\Delta\)PV values were detected, with a significant increase towards a maximum already after 4 days of storage, followed by a significant decrease after 8 days. Since a clear and early primary oxidation peak was observed, respectively, with \(\Delta\)PV maxima of 341 (\(\pm\)108), 402 (\(\pm\)79), and 491 (\(\pm\)46) meq hydroperoxides/kg lipids for 'Mixture 6/94', 'Mixture 1/99', and 'Pure Fish oil', these systems were only analyzed during 4 weeks. Based on these results, it could be stated that regarding primary oxidation, 'Mixture 79/21' and 'Mixture 49/51' were in line with the pure _Phaeodactylum_ extract, of which the tendency not to lead to high \(\Delta\)PV values was comparable with former research following primary oxidation during storage of _Phaeodactylum_ hexane-isopropanol (3:2) extracts [16]. 'Mixture 6/94' and 'Mixture 1/99', on the other hand, were in line with the pure fish oil, for which a clear primary oxidation peak showed its high oxidative susceptibility. Comparing the primary oxidation results of the pure fish oil with those of Part I (3.1.1.), it was clear that the \(\Delta\)PV peak occurred sooner. This could be attributed to the fact that in Part II amounts of 10 mg/vial instead of 1 g/vial were used, leading to a higher oxygen to bulk oil availability, explaining the accelerated oxidation processes [42]. [PERSON] et al. [16] also observed a primary oxidation peak after 7 days (as the first time point) for refined fish oil being stored at 37 \({}^{\circ}\)C in amounts of 10 mg/vial with a weekly PV analysis.
## 3 Results and Discussion
### Recommendations for Adjusting the Parameters of the Ultrasonic Muffler  
The analysis of the obtained data indicates that the efficiency of ultrasonic exhaust gas cleaning largely depends on ultrasonic emitter power (\(N\)) and the spacing between emitters (\(L\)). As the engine speed increases, the exhaust gas flow velocity rises, necessitating higher ultrasonic power to compensate for the reduced interaction time between particles and ultrasonic waves. At the same time, with higher flow speeds, the optimal distance between the emitters decreases, as a denser arrangement facilitates more effective particle interaction with the particles, preventing them from exiting the muffler without coagulation.  
These findings suggest that optimizing the ultrasonic cleaning process requires dynamic adjustment of both the emitter power and spacing based on the engine's operating conditions, specifically considering the engine speed and exhaust flow characteristics. Balancing these parameters ensures more efficient particle coagulation and PM reduction in the exhaust gases.  
Based on the regression model, the optimal power for effective impact at 1800 rpm is \(N=319.35\) W. Given this, when using two emitters, their power should be approximately 160 W each. However, for improved exhaust flow coverage and enhanced coagulation efficiency, the use of three emitters is preferable, each operating at 100-110 W and spaced 0.8-0.9 m apart. This setup allows for a stepwise treatment of PM particles, impacting them at different levels and increasing the efficiency of their deposition.  
Additionally, for maximum reduction in smoke levels, the silencer design can be further improved by adjusting the frequency and amplitude of the ultrasonic waves, enabling better adaptation to various engine operating modes. Furthermore, the use of multiple emitter levels in a vertical configuration will enable staged gas cleaning, reducing the likelihood of previously coagulated particles re-entering the gas stream.  
The use of such a silencer is relevant not only for passenger vehicles but also for trucks, agricultural machinery, and stationary diesel installations, where the issue of reducing harmful emissions remains an ongoing challenge. Moreover, the design can be adapted to different engine types by adjusting the emitter power and positioning according to specific operating conditions. This makes the ultrasonic silencer a flexible and promising solution for smoke reduction in a wide range of transportation and industrial applications.
## Abbreviations  
\begin{tabular}{l l l}  & MRT & Maximum ratio transmission \\ AEH & Adaptive energy harvesting & MTC & Machine-type communication \\ A2G & Air-to-ground & NLoS & Non-line-of-sight \\ AoI & Age of information & NOMA & Non-orthogonal multiple access \\ AO & Alternating optimization & OF & Objective function \\ AR & Arrey response & PPO & Proximal policy optimization \\ BCD & Blocked coordinate descent & PSO & Particle swarm optimization \\ BF & Beamforming & P-BCD & Penalty-based block \\ BS & Base station & RHS & Reconfigurable holographic surface \\ CDF & Cumulative distribution function & RIS & Reconfigurable intelligent surface \\ CLoS & Cascaded line-of-sight & RISeUAV & RIS-equipped UAV \\ DDPG & Deep deterministic policy gradient & RIS-PhSh & RIS Phase Shift \\ DDQN & Double deep Q-network & RSMA & Rate-splitting multiple access \\ DFT & Discrete Fourier transform & SA & Simulated annealing \\ DQN & Deep Q-network & SDP & Semi-definite programming \\ DR & Data rate & SEE & Secrecy energy efficiency \\ DRL & Deep reinforcement learning & SIC & Successive interference cancelation \\ EE & Energy-Efficiency & SINR & Signal-to-interference-plus \\ EH & Energy harvesting & SCP & Sequential convex programming \\ FFM & Fourier feature mapping & SCA & Successive convex approximation \\ FSPL & Free Space pathloss & SDMA & Space division multiple access \\ FHRE & Fronthaul-rate-ensuring & SNR & Signal-to-noise ratio \\ HAP & High-altitude aerial platform & STAR-RISs & Simultaneously transmitting and \\ GA & Genetic algorithm & SWIPT & Simultaneous wireless information \\ GRP & Gaussian randomization procedure & T-UAV & Tethered-UAV \\ G2G & Ground-to-ground & UAV & Unmanned aerial vehicle \\ GU & Ground users & UAV-BS & UAV as BS \\ LAP & High-altitude aerial platform & UE & User equipment \\ LoS & Line-of-sight & ULA & Uniform linear array \\ IOS & Intelligent omni-surface & UPA & Uniform planar array \\ MAB & Multi-armed bandit & WCSR & Worst-case secrecy rate \\ MAT & Multiple antenna & WIT & Wireless information transfer \\ MDP & Markov decision process & WOA & Wales optimization algorithm \\ MEC & Mobile edge computing & WPT & Wireless power transfer \\ MIMO & Multi-input multi-output & WSDR & Weighted sum data rate \\ MINP & Mixed integer nonlinear program & WSR & Weighted sum-rate \\ \end{tabular}
# A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller
## 4 HMM-R Detection Scheme
### Model Detecting  
We collected the PACKET_IN packets under a window with a certain time by SDN, then preprocess these packets, and get the Renyi entropy of source and destination IP. These preprocessed data and k-means clustering data are calculated for Euclidean distance, as shown in Formula (7).  
\[\begin{array}{l}d(x_{i})=\frac{\sum\limits_{j=1}^{n}\sum\limits_{i=1}^{n}\left( c_{ij_{i}}-x_{ij_{j}}\right)^{2}}{n}\\ OF(x)=\operatorname*{argmin}_{1\leq i\leq k}d(x_{i})\end{array} \tag{7}\]  
In Formula (7), \(C_{ij_{2}}\) represents the \(z\) dimension of the \(j\) data in class \(i\); \(m\) represents the dimension of data and \(m\) is \(2\); \(n\) represents the total number of data in each class; \(k\) represents the total number of k-means clustering; \(OF(x)\) represents the category of a data.  
We calculate the Euclidean distance for Renyi entropies of source IP and destination IP, and select the minimum Euclidean distance as the state of the detection data, that is, the category. The Viterbi algorithm is used to solve the problem of HMM-R decoding, the specific process is shown in Figure 4. We needed to obtain the five model parameters of HMM-R: the state space, the observation state space, the state transition matrix, the observation transfer matrix and the initial probability transfer matrix, to detect the prediction of observed data.  
Figure 4: Viterbi algorithm process.  
The HMM-R detection algorithm is proposed as Algorithm 1:  
```
Input: \(\lambda\), \(T\), \(W\) Output: \(S\)
1. \(P\_C\gets 0\), \(COUNT\gets 0\), \(O\leftarrow\{\ \}\), \(Src\_Ip\leftarrow\{\}\), \(Dst\_Ip\leftarrow\{\}\)
2. FOR \(i\gets 1\) to \(|X|\)
3. \(\delta_{1}(i)\leftarrow\pi_{i}b_{i}(o_{1}),\psi_{1}(i)\gets 0\)
4. IF \(P\_C<=W\) THEN
5. \(Src\_Ip\gets Src\_Ip+Packet\_In\_OpenFlow(event)\)
6. //Function \(Packet\_In\_OpenFlow(event)\) handles \(Packet\_In\) messages
7. \(Dst\_Ip\gets Dst\_Ip+Packet\_In\_OpenFlow(event)\)
8. \(P\_C\gets P\_C+1\)
9. ELSE
10. IF \(COUNT<=T\) THEN
11. \(Src\_Ent,Dst\_Ent\gets Entropy(Src\_Ip,Dst\_Ip)\)
12. //\(Entropy()\) is a function to calculate Renyi entropy
13. \(COUNT\gets COUNT+1\)
14. \(O\gets O+(Src\_Ent,Dst\_Ent)\)
15. ELSE
16. \(O\gets Compute(\lambda,O)\) //\(Compute()\) is a function to calculate Euclidean distance
17. \(S\gets Viterbi(\lambda,O)\)
```  
**Algorithm 1** HMM-R detection algorithm  
Table 1 shows the meaning of the variable names in Algorithm 1.
## 3 Geophysical Techniques and Geophysical Characteristics
### Geophysical Techniques
#### 3.1.4 Natural Source Surface Wave Microtremor  
There is a weak continuous natural surface wave signal source in the human activity area near the earth's surface, scholars have used it to detect and calculate the seismic velocity structure of the stratum since the 1950s. At present, low-frequency signals generated by tides, waves, pressure changes and high-frequency mechanical vibration signals generated by human activities are mainly used to deduce the subsurface transverse wave velocity model based on the dispersion curve inversion of these Rayleigh surface wave characteristics [10]. In this study, a WD-1 intelligent natural source surface wave data acquisition system funded by the Beijing Institute of Hydroelectricity Geophysical Exploration was used. Data collection was carried out using a 2 Hz detector 10-channel equilateral triangleembedded pole mode. The maximum side length is 90 m, the sampling interval is 5 m and the number of iterations is 600.
# Risk-Cost Optimized Maintenance Strategy for Steel Bridge Subjected to Deterioration
## 5 Corrosion Models  
The deterioration of steel girders is caused by corrosion. It is widely accepted to use average corrosion loss (thickness loss) to represent the corrosion degree of bridge girders [39]. It is assumed that corrosion happens uniformly across girders. The average corrosion loss can be calculated as follows [39]:  
\[C_{r}(t)=Kt^{n} \tag{23}\]  
where \(C_{r}(t)\) is the average corrosion penetration in micrometers (\(10^{-3}\) mm) at time \(t\). The model parameters \(K\) and \(n\) were determined as 0.0802 mm and 0.593 for the structural components in an urban environment [39]. Based on Equation (23), the changes in geometry of steel girders over time can be determined.  
It can be seen from Equations (11), (13) and (16) that the changes in yield strength \(\sigma_{ye}(t)\), fatigue strength coefficient \(A(t)\), and fatigue strength exponent \(B(t)\) over time from corrosion can be determined as follows for low-carbon bridge steel (e.g., G250 steel) [2,28].  
\[\sigma_{ye}(t)=\sigma_{yoo}(1+0.003428C_{r}(t)-0.034498C_{r}(t)+0.006139), \tag{24}\]  
\[A(t)=-9\times 10^{20}cr(t)+1\times 10^{20}, \tag{25}\]  
\[B(t)=-12.59 cr(t)+5.4589 \tag{26}\]  
where \(\sigma_{yoo}\) is the yield strength of steel before corrosion, and \(cr(t)\) is the corrosion rate, which can be determined as follows:  
\[cr(t)=\frac{dC_{r}(t)}{dt}=Knt^{n-1}. \tag{27}\]
## 3 Results
### CORINE Land Surface Cover Database  
The data in the CORINE Land Cover database are recorded in the 1 st half of the 21 st century. The CORINE database was uploaded into the ArcGIS software package, and then, data processing and analysis were performed. CORINE forest cover was compared with data obtained from old topographic maps. Three classes were used in the study--\"deciduous forests\", \"coniferous forests\", and \"mixed forest\", which were combined into one class \"forest cover\". Attribute data were selected according to the codes defining the forest by applying the analysis methods integrated into the GIS program, and exported to separate shapefiles. The data format was unified with other available data formats and data analysis was performed. Forest covers and areas for each eldership and Jurbarkas District have been determined. The area occupied by forest cover was calculated from the areas of administrative units, as a percentage (Table 6).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Elderships** & **The Area of Forest Cover, \%** \\ \hline Erzvilkas & 24.1 \\ Girdziai & 26.9 \\ Juodaiaiai & 17.5 \\ Jurbarkai & 50.6 \\ Jurbarkas city & 15.3 \\ Raudôe & 22.0 \\ Seredzius & 33.6 \\ Skirsnemuné & 14.4 \\ Smalininkai & 39.2 \\ Simkaixiai & 24.3 \\ Veliuona & 33.5 \\ Viesville & 67.6 \\ Jurbarkas Distr. & 33.9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Areas of forest cover in the 1 st half of the 21 st century as a percentage of the area of the elderships.  
The area of forest cover in the elderships is 17.5-67.6% of the areas of the elderships in the 1 st half of the 21 st century. The average is 30.7%.  
The forest covers more than 30% of the territory in five out of twelve elderships. The forest covers only 17.5-26.9% of the area of the elderships in the remaining eight elderships.  
Forest coverage in Jurbarkas District is up to 33.9%. Forest coverage exceeding 30% is achieved only thanks to two elderships--Jurbarkas and Viesvile. The average forest coverage of the studied territory in the remaining elderships without said two elderships, would be only 25.7%.  
The created forest cover maps are presented in Figure 12 for the elderships and in Figure 13 for Jurbarkas District.
# Application and Performance Evaluation of Resource Pool Architecture in Satellite Edge Computing
###### Abstract  
Satellites will play a vital role in the future of the global Internet of Things (IoT); however, the resource shortage is the biggest limiting factor in the regional task of massiveequipment in the IoT for satellite service. Compared with the traditional isolated mode of satellite resources, the current research aims to realize resource sharing through satellite cooperation in satellite edge computing, to solve the problems of limited resources and low service quality of a single satellite. We propose a satellite resource pool architecture-oriented regional task in satellite edge computing. Different from fixed servers in ground systems, the satellite orbital motion brings challenges to the construction of the satellite resource pool. After the capacity planning of the satellite resource pool for regional tasks is given, an algorithm based on search matching is proposed to solve the dynamic satellite selection problem. A ground semi-physical simulation system is built to perform experiments and evaluate the performance of three modes of satellite resource sharing: isolated mode, cooperative mode, and pooled mode. The results show that the pooled mode, compared with the isolated mode, improves the task success rate by 19.52%, and at the same time increases network resources and energy consumption in the same scenario. Compared with the cooperation mode, the performance of task success rate and resource utilization rate is close to that of the pooled mode, but it has more advantages in response time and load balancing of satellite resources. This shows that in the IoT, the resource pool is of great benefit as it improves the task response time and improves the load balance of satellite resources without degrading the performance, which makes sense in task-demanding scenarios.  
s +
Footnote †: journal: Aerospace  
article edge computing; satellite resource pool; resources sharing; capacity planning; performance evaluation; load balance