esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Simulation Results
### Investigating the Performance of the Suggested Objective Functions
As a further discussion on the results depicted in Figures 5 and 6, in case 2, more load-shedding steps are activated as compared to case 1. In case 1, both the initial and the minimum set frequencies were lower compared to case 2. As a result, we would expect that in case 1 there will be a delay in implementing the load-shedding as evident in Figure 5. In both cases in the first step a large amount of load is shed to quickly arrest the frequency decline however in the second step of case 2 a small load is shed so that the frequency overshoot is maintained within the set limits. If a large load is shed in step 2 of case 2 the overshoot will be higher. Since a small amount is shed in step 2, an additional load-shedding stage is activated in case 2 as compared to case 1. In cases (\(C_{3}\) & \(C_{4}\)), the optimization method should determine the load amount to be shed and at which frequency the load-shedding should be done as shown in Table 3. Therefore, LB and UB of frequency at each stage are pre-set as shown in the same Table. Figure 7 shows that in case 4 the minimum frequency with load-shedding was nearly 48 Hz and in case 3 it was almost 49 Hz. The same number of load-shedding stages were activated in both cases with case 3 shedding more as shown in Figure 8. From the frequency settings shown in table, the minimum frequency for case 3 was less therefore it was more likely that less load should be shed to restore the system to nominal operating levels. In both cases, the frequency at which shedding is to be done and the amount of load to be shed was determined by the optimization algorithm. The lower and the upper limits for load to be shed were 0 and 0.2 respectively for each and every stage. Figure 5: The amount of load to be shed in the first scenario: a comparison between cases 1 & 2. Figure 6: Frequency behavior in the first scenario: a comparison between cases 1 & 2.
## 4 Experimental Results and Discussion
### Experimental Results
#### 4.3.1 Analysis of Experimental Results  
The proposed ISOD algorithm achieved the best performance, with an average ROC of 0.813 and an average precision of 0.75. As shown in Table 2, the ISOD algorithm achieved the highest ROC in 6 of the 10 datasets. Additionally, as shown in Table 3, the ISOD algorithm achieved the highest AP (average precision) in 6 of the 10 datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Number of Samples** & **Number of Dimensions** & **Outliers (\%)** \\ \hline Musk & 3062 & 166 & 97 (3.2\%) \\ \hline Satimage-2 & 5803 & 36 & 71 (1.2\%) \\ \hline Letter Recognition & 1600 & 32 & 100 (6.25\%) \\ \hline Speech & 3686 & 400 & 61 (1.65\%) \\ \hline Satellite & 6435 & 36 & 2036 (32\%) \\ \hline Arrhythmia & 452 & 274 & 66 (15\%) \\ \hline Ionosphere & 351 & 33 & 126 (36\%) \\ \hline Mnist & 7603 & 100 & 700 (9.2\%) \\ \hline Optdigits & 5216 & 64 & 150 (3\%) \\ \hline Heart & 224 & 44 & 10 (4.4\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ten real-word benchmark datasets.  
It is worth noting that, by analyzing the data in Tables 2 and 3, it can be found that the higher the data dimensionality, the better results the ISOD algorithm can achieve, as exemplified by the results for the Speech, Satellite, and Arrhythmia datasets. This confirms that the ISOD algorithm has low time complexity and good performance when working with data with high dimensionality, as noted in Section 3.4.1.
# Progress in Energy Storage Technologies and Methods for Renewable Energy Systems Application
## 3 The Progress of Energy Storage Technologies
### Lithium-Ion Batteries  
Lithium-ion batteries are most widely used in modern digital electronics. Among them, lithium iron phosphate batteries have a longer cycle life, high energy density (200 W h/kg), environmental protection, and affordable and good charge and discharge performance (over 95%). They are widely used in electric vehicles and scaled energy storage [61]. Li-ion batteries have millisecond response times, high energy densities, and high cycle efficiencies (up to 97%). However, there are still some problems with their safety, such as the explosion caused by thermal runaways [62]. At present, the research focus of lithium-ion batteries includes cathode consideration towards high nickel NCM/NCA, negative electrode consideration towards Si material, and electrolyte development towards an electrodeless solid electrolyte. The U.S.-based AES Energy Storage already operates a lithium-ion BES system commercially in New York, and AES installed a 32 MW/8 MWh lithium-ion BES system (Laurel Mountain) to support a 98 MW wind farm in 2011 [9]. [PERSON] et al. [63] proposed a lithium-sulfur battery, which uses a nanostructured sulfur composition. Three mainstream electrodes were compared, and 3,4-ethylene-dioxythiophene was found to have the highest long-term reactivity and performance. [PERSON] et al. [64] used a nanofiber material surface to stabilize the performance of more than 300 cycles, with an 80% capacity retention to improve the cycle life of lithium-sulfur batteries. For the study of lithium-ion battery modeling, the following stages were mainly used: the integer model, fractional-order electrical model, fractional-order electrochemical model, and fractional-order thermal model. In particular, for the RC model, the first order circuit mimics ohmic resistance and charge transfer, and the second order mimics ohmic resistance and diffusion behavior. Third-order circuits, although they can produce the full range of processes, employ a larger set of parameters, also risking overfitting [59].
## 3 Results
### Effect of LE on AMPK-Dependent ROS Generation and DNA Damage  
The effect of AMPK signaling on ROS generation was evaluated by pretreating cells with the AMPK inhibitor compound C. The H\({}_{2}\)O\({}_{2}\)-mediated increase in the DCF-DA staining cells (to 41.99%) was reduced (to 13.81%) by LE but restored (to 33.74%) by compound C (Figure 6a).  
Figure 5: Effect of LE on AMPK signaling in C2C12 cells: (**a**) Effect of LE on the AMPK\(\alpha\)1/2, p-AMPK\(\alpha\) (Thr172), ACC, and p-ACC (Ser79) levels of total cell lysates. Western blot analysis was performed using \(\beta\)-actin as a loading control. Representative results from three independent experiments; (**b**) Effect of LE (1 h) and H\({}_{2}\)O\({}_{2}\) (0.8 mM for 24 h) on AMPK\(\kappa\)1/2 and p-AMPK\(\alpha\) (Thr172) expression. Western blot analysis was performed using \(\beta\)-actin as a loading control. Representative results from three independent experiments; (**c–e**) Cells were pretreated using 5 \(\mu\)M compound C, with or without 10 \(\mu\)g/mL LE, for 1 h and then treated with 0.8 mM H\({}_{2}\)O\({}_{2}\) for 24 h; (**c**) Cell viability was assessed using the MTT assay. Data indicate mean \(\pm\) SD values (\(n=3\); *** \(p<0.001\) vs. control cells; *** \(p<0.001\) vs. H\({}_{2}\)O\({}_{2}\)-treated cells; *** \(p<0.001\) vs. H\({}_{2}\)O\({}_{2}\) and LE treated cells); (**d**) Cell morphological changes. Representative results of three independent experiments, which were observed using phase-contrast microscopy, are shown; (**e**) AMPK\(\alpha\)1/2 and p-AMPK\(\alpha\) (Thr172) expression. Western blot analysis was performed using \(\beta\)-actin as a loading control. Representative results from three independent experiments are shown. AMPK, AMP-activated protein kinase; ACC, acetyl-CoA carboxylase; Com C, compound C.  
The role of the AMPK pathway in the protective effects of LE against H\({}_{2}\)O\({}_{2}\)-induced DNA damage was investigated using comet analysis and the pretreatment of cells with the AMPK inhibitor compound C. DNA damage was clearly induced by H\({}_{2}\)O\({}_{2}\) treatment (Figure 6b). The extents of DNA damage were manually measured and are shown in Figure 6c. However, DNA tails were not observed during the analysis of cells treated in the presence of LE, and this protective effect was attenuated by compound C. In addition, H2 AX expression was not significantly affected by pretreatment with compound C (Figure 6d). However, \(\gamma\)H2 AX expression level was elevated by H\({}_{2}\)O\({}_{2}\) treatment and reduced in the presence of LE, and the protective effect of LE against H\({}_{2}\)O\({}_{2}\)-induced DNA damage was attenuated by compound C. These results suggest that the AMPK-dependent protective effect of LE against H\({}_{2}\)O\({}_{2}\)-induced DNA damage is related to the inhibition of ROS generation.
# Light-Controlled Microbots in Biomedical Application: A Review
## 5 Imaging
### Optical Imaging  
Before imaging with an optical system, there are some things that must be in order. There are several advantages and disadvantages to employing an optical imaging system. The light must travel in a straight line through all available mediums before reaching the camera sensor or the eyes. The camera sensor, camera filter, objectives for adequate magnification, various light sources, and dyes for acceptable picture contrast are only a few of the critical components of an optical imaging system. Optical imaging is extremely difficult because of so many parameters. The prospect of optical photographic imaging is reviewed in detail by [PERSON] and his team [205].  
If the workspace is transparent where light can pass through, then imaging with a camera (CCD/CMOS) camera is a viable option. Often, a camera is used for auxiliary control and localization of the robot. Imaging systems primarily track the motion of any particles in the workspace, which is vital in controlling and actuating the movements of microbots. However, in in vivo application, imaging in the visible wavelength range might not be an ideal choice, as it is nearly impossible for visible light to penetrate through the tissue of the body. From the figure, it can be seen that only a few hundred nanometers of penetration through the skin is possible with visible light. If we want to penetrate through the tissue, then the wavelength of the light has to be beyond the NIR wavelength. Therefore, Ultrasound, X-ray fluoroscopy, Photoacoustic Computed Tomography (PACT), positron emission tomography (PET), etc. are needed to obtain decent imaging of the workspace. Incorporating these additional features in the microbot actuation increases the complexity along with the overall capability of the system.  
If the camera is used with a microscope, it is critical to utilize the appropriate objective lens with the right magnification. It is preferable if there are many magnification settings available so that the exact position of the microbots may be located faster. Particularly for optical trapping, an objective with a higher numerical aperture is required to focus the collimated laser light properly.  
Different light sources might also be required to illuminate the workspace to capture a bright image. Optical trapping with an inverted microscope uses a white LED light to illuminate the sample. For better imaging, along with the white LED, a Halogen light source is also used to get a better contrast of the image and distinguish the microbot from the medium of the workspace. Sometimes different dyes are used to get the best results during imaging. Furthermore, fluorescence coating is a clever idea to image micro-robots in operation [206; 207].
## 2 Materials and Methods
### Topology Optimization Analysis
#### 2.5.1 Variable Density Method  
In the variable density method, the density of the finite element of the optimization model is regarded as changing. Additionally, the relative density of the model varies within the interval (0, 1) with the force condition. The penalty function is introduced. By setting the threshold value, Ansys Workbench software will delete densities of the finite element less than the threshold value, while those greater than the threshold value will be retained. The interpolation model used in this paper is Solid Isotropic Material with Penalization (SIMP). In the model, the material of the optimization model is isotropic, and the density and properties of the material are exponential functions. The primary expressions of SIMP are:  
\[E(\rho)=\rho pE_{0},\rho\in[0.001,1] \tag{3}\]  
\[E(\rho)=E_{\min}+\rho p(E_{0}-E_{\min}),\rho\in[0.001,1] \tag{4}\]  
In the formula, \(\rho\) is the relative density of elements, \(p\) is the penalization parameter, which is the power of the density, \(E\) is the elastic modulus after interpolation, \(E_{0}\) is the solid elastic modulus, and \(E_{\min}\) is the elastic modulus at the cavity. \(E_{\min}\) is set to \(E_{0}/1000\).  
The parameters \(\rho\) and \(p\) control the relative density of elements; the higher the value of \(p\), the more the intermediate density is penalized, and the more the relative density of elements will approach 0 or 1. If the value of \(p\) is too high, the function will be difficult to converge. Therefore, the value of \(p\) is generally within the interval (2,6). In Ansys Workbench, the default penalty factor \(p\) is 3.
###### Abstract
Spatial domain encryption [9] is a common method and is divided into spatial domain scrambling and sequence encryption, where Arnold Transform [10] is a widely used image scrambling algorithm. The proposed method performs pixel sequence scrambling of the RGB components of a color image using the Arnold Transform. Next, applying the ElGamal Encryption Algorithm [11] to continue the secondary encryption, a Public Key Cryptographic algorithm based on discrete logarithms is obtained. The Public Key Cryptographic algorithm solves two of the most difficult problems in Single-Key Cryptosystems: the key's distribution and digital signatures. Therefore, the Public-Key Cryptographic algorithm ElGamal is chosen for this work, which is superior in the key distribution and signature authentication parts. Another reason for choosing the ElGamal Encryption Algorithm is that it is based on discrete logarithms. The security of discrete logarithm cryptographic algorithms follows mathematical puzzles, such as the discrete logarithm problem [12] and the elliptic curve discrete logarithm problem [13], that are difficult to break even if the nature of the algorithm is known. Here, the ElGamal algorithm based on the discrete logarithm algorithm is also a very good digital signature authentication algorithm, providing strong support for the purpose of the proposed model dedicated to building a complete authentication process in the storage and transmission of biometric images. Digital signatures include authentication, data integrity, non-repudiation, and anonymity. The proposed model uses the ElGamal algorithm to digitally sign the original biometric image before encrypting it and verifying it after decryption to ensure that its identity and content have not been tampered with. The main novelty and contribution of this paper are 1. Innovated the common 2D Arnold Transform into its 3D Arnold Transform, allowing various parameters to be used before being nested with the encryption from the ElGamal Encryption Algorithm for enhanced algorithmic security. 2. Proposed a complete encryption, decryption, and authentication process that satisfies both encryption and authentication as the two main security requirements for the application of biometric data preservation. The rest of the paper is described as follows: Section 2 presents the background. Section 3 elaborates on the methodology and mathematical analysis. Section 4 introduces the process of the experiments. Section 5 introduces and discusses the results. Finally, Section 6 concludes this paper.
## 3 Methodology
### Critical Infrastructure Modeling  
The CIs being investigated are modeled as a connected graph, in which each functioning component is considered as a node in the network, while the relationships between components in the network are defined as edges. For a network \(G\), we define the set of nodes as \(V\), within which each node is defined as \(v_{i}\) (\(i=1,\ \cdots,\ n\)), and the set of edges as E, where each edge is defined as \(e_{j}\) (\(j=1,\cdots,m\)). In complex network theory, for any damage \(D\) that occurs to the network \(G\), the functionality of the network confronting this interference can be expressed as:  
\[V(G,\ D)=\frac{\Phi(G)-\Phi(D(G))}{\Phi(G)} \tag{1}\]  
where \(\Phi(G)\) is defined as the betweenness of network \(G\), and is calculated as:  
\[\Phi(G)=\frac{1}{n(n-1)}\sum_{j=1}^{m}\frac{1}{r_{j}} \tag{2}\]  
where (\(r_{j}\)) is the level of resistance in the system on edge \(e_{j}\) as compared to full connection. In network theory, the level of betweenness also represents the global efficiency of the network system, while the vulnerability of the network is conceived as the level of reduction in global efficiency arising from the failure of nodes and/or edges in the network [22].  
Following on from the above definitions, terrorist attacks can affect the functionality of the system in two ways. First, when a functioning component (\(v_{i}\)) of the critical infrastructure becomes damaged,  
Figure 1: Protection Strategy Analysis Framework.  
the level of resistance of the connections in all edges that are linked to the damaged component will increase. Second, by damaging a particular connecting edge (\(e_{j}\)), the level of resistance to the connectivity of the damaged edge can increase. In both cases, a decrease in functionality in the whole system will occur.  
For each attack strategy \(a_{k}\) on the critical infrastructure, the resistance level of the edges being interfered with will change; the post-attack resistance level can therefore be defined as \(r_{j}(a_{k})\). However, the changes in resistance due to attack \(a_{k}\) are also influenced by protective actions \(p_{l}\), so the post-attack robustness level in light of such actions is expressed as \(r_{j}(a_{k},\,p_{l})\). Thus, the vulnerability of the infrastructure given protective actions \(p_{l}\) and attack \(a_{k}\) can be calculated from Equations (1) and (2).
## 2 Materials and Methods
### Degradation
#### 2.3.1 TBNPA and DBNPG Degradation Kinetics with Yeast Extract  
Degradation kinetic experiments were carried out in a 450 mL growth medium (see Supplementary Materials) inoculated with 50 mL of the active four-strain consortium at the stationary phase (OD600 \(\approx\) 0.07), with an initial TBNPA or DBNPG concentration of 100 mg L\({}^{-1}\). Experiments were conducted in duplicates and accompanied by an abiotic control. During incubation, 2 mL samples were analyzed for TBNPA or DBNPG concentrations until the complete disappearance of TBNPA or DBNPG.
## 2 Experiments
### Electrochemical Tests  
A CHI600E electrochemical workstation (Chenhua Instrument Co., Ltd., Shanghai, China) was utilized to perform the electrochemical tests, employing a standard three-electrode system. The electrochemical tests in this paper did not use in situ test methods. Following the immersion, the N80 steel sample was taken out of the autoclave and subjected to electrochemical testing under ambient temperature and pressure. The solution used in the electrochemical tests was the same as the solution used in the immersion tests. The working electrode in this configuration was N80 steel, the reference electrode was a saturated potassium chloride calomel electrode, and the auxiliary electrode was a platinum sheet. The electrochemical test parameters in this experiment primarily encompassed the open-circuit potential (OCP) evaluation, the potentiodynamic polarization assessment (Tafel curve), and the electrochemical impedance spectroscopy (EIS) analysis. The corrosion characteristics of N80 steel were investigated using these three electrochemical test methods. Prior to conducting EIS and potentiodynamic polarization analyses, the OCP was monitored for 1 h. A sinusoidal potential perturbation with a 5 mV amplitude was applied over a frequency range of 10\({}^{-2}\) Hz to 10\({}^{5}\) Hz at the OCP for the EIS measurements. Meanwhile, the potential was swept from \(-\)500 mV against OCP to +500 mV against OCP using a sweep rate of 10 mV/s for the potentiodynamic polarization test.
## 4 Result and Discussion
### Discussion  
Our study demonstrates that the wildfire events that occurred in 2019 at the Brazilian Amazon significantly impacted the primary productivity of the forest, as well as the Amazonian ecosystem, compared to 2018. We estimated an 8.95% reduction in GPP during 2019 at the 12 pilot study plots in the Brazilian Amazon compared to 2018. Amazon forest is globally recognized as a prime producer of oxygen and receiver of carbon. However, such destructive wildfire events often turn this carbon sink zone into a carbon source, due to wildfires. It has also been observed that though wildfire events do not hamper the general trend of GPP for the forest ecosystem, they significantly reduce forest productivity, resulting in low consumption of carbon for photosynthetic activity and a decrease in autotrophic and heterotrophic respiration [13]. Thus, it can be assumed that the ecosystem respiration over the different wildfire zones of the Amazon forest during 2019 decreased due to forest fires and induced extreme heat. Such disturbances, particularly extreme wildfires events like in 2019, significantly hampers the structure and diversity of the landscape over time by exerting selection pressure, controlling succession, and affecting the ecosystem functioning, including the carbon and nutrient cycles [14]. Thus, it is evident that the wildfire events not only disturbed the structure, composition, and functionality  
Figure 4: Site-wise comparison of GPP between 2018 and 2019. Black straight lines on the bars show the standard deviations.  
Figure 5: Loss in productivity in terms of GPP during 2019 compared to 2018 at all 12 pilot study sites.  
of the terrestrial ecosystem, but also influenced the global CO2 fluxes and their feedback to the global climate system as a whole [15].
## 3 Optimization Design  
### Design Parameter  
#### 3.1.1 Material Parameter  
The selection of materials is crucial for the performance of CMs. Excessive flexibility may reduce the overall rigidity of the mechanism, adversely affecting its dynamic performance and precision. Conversely, excessive rigidity may limit the working stroke of the mechanism. In the SRF, the mechanism is subjected to ultra-high vacuum and high-energy radiation for prolonged periods. Considering the high cost of the load crystals in the nano-adjustment mechanism, the choice of material becomes critical. Beryllium bronze (QBe2), known for its superior hardness, elastic limit, fatigue limit, wear resistance, and corrosion resistance, is an ideal material for CMs. After a solid solution treatment at 800 \({}^{\circ}\)C followed by an aging treatment at 330 \({}^{\circ}\)C, beryllium bronze demonstrates enhanced mechanical performance and corrosion resistance. The specific material performance parameters are detailed in Table 1.