esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works  
### Wireless Energy Transfer  
In the year 2007, [PERSON] from the Massachusetts Institution of Technology (MIT) published a breaking-through paper [2] in Science discussing transferring energy through magnetic fields between two coupled coils. The wireless energy transfer method introduced by this MIT research team has high efficiency and can be performed from a relatively long distance. Later on, this team co-founded the WiTricity Cooperation and manufactured several different types of wireless energy transfer devices, such as Prodigy, WiT-5000, WiT-3300, _etc._ Recently, many researchers enriched the theoretical results and practical usages of the newly-brewed wireless energy transfer technique. [PERSON] _et al._ found a way to make a three-coil wireless energy transfer system more efficient than a two-coil one [3]. Experiments were also conducted by [PERSON] _et al._ to show the efficiency of wireless energy transfer between unsegmented and segmented coupling coils [4]. [PERSON] _et al._ wrote a paper introducing the potential usage of wireless energy transfer for electrical vehicles [5]. Wireless energy transfer may also be adopted to deliver energy to some in-body medical parts, such as heart pacemakers instead of complicated open chest surgery [6].  
The wireless energy transfer method proposed by [PERSON] has several advantages compared to previous ones. First of all, this method adopts a non-radiative way to deliver energy between the transferor and transferees efficiently, even if there are ferrous obstacles amongst them. Secondly, the transfer range can be further increased by installing repeaters between the transferor and transferees. Moreover, this type of energy transfer has high directionality, _i.e._, the energy delivery only succeeds among coils of the same magnetic resonance frequency. Besides, the energy transfer process has almost no interference with wireless communications, which makes it a suitable way to replenish the energy of sensor nodes. [PERSON] _et al._ developed a data telemetry device, which can be charged remotely by a wireless energy transfer technique [7]. What is more, fortunately, the non-radiative nature of this wireless energy transfer technique will not emit electromagnetic waves and, hence, will not probably induce fire accidents or explosions, which is a huge advantage with respect to dealing with the safety-related issues in the complicated underground environment.
## 4 Materials and Methods
### CNN Training for Wildfire Prediction  
Convolutional Neural Networks (CNNs) are widely used in image-based wildfire prediction, because they capture spatial dependencies in data [43]. This model is particularly effective when dealing with satellite imagery and geospatial information.  
The training procedure for the CNN model is available in the Supplementary Material (Algorithm S2), where the network learned hierarchical feature representations to optimize wildfire prediction accuracy. Some advantages of CNNs include their efficiency in feature extraction and their robustness when dealing with geospatial datasets. However, CNNs struggle to capture temporal dependencies, which limits their performance in predicting the progression of wildfires over time.
# Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber-Physical Production Systems
## 2 Background and Related Works
### Common Vulnerabilities in CPPSs  
CPPSs encounter distinct security challenges that set them apart from traditional IT systems, stemming from their intricate networks and heterogeneous embedded components [26]. We conducted a static analysis of existing vulnerabilities affecting common CPPS assets (i.e., human-machine interface (HMI), programmable logic controller (PLC), remote terminal units (RTUs), and intelligent electronic devices (IEDs)). For this analysis, we primarily utilized two data sources: NVD, and Shodan. We then summarized the vulnerabilities commonly exploited in CPPSs, classifying them according to the common weakness enumeration (CWE) [27]:  
* Software and firmware vulnerabilities: Flaws in application software and operating systems are prevalent yet challenging to mitigate. Vulnerabilities such as outdated firmware (e.g., CWE-1277) open back doors for attackers. Programming errors leading to buffer overflow can enable unauthorized code injection and elevated system access.
* Data communication security: The use of unencrypted protocols for data transmission risks, exposing sensitive information to unauthorized interception; vulnerable to man-in-the-middle (MiTM) attacks, as exemplified by the deployment of HTTP basic authentication for sensitive data (CWE-319).
* Access control issues: Inadequate access control mechanisms, such as improperly granting administrative permissions to guest accounts, can compromise critical system files. This encompasses flaws in identity management (e.g., CWE-1294), resource isolation (e.g., CWE-1189), authentication (e.g., CWE-261), and authorization (e.g., CWE-732). The integration of IoT devices introduces further hardware-targeted threats, including improper resource control (e.g., CWE-125 and CWE-787).
* Cybersecurity awareness and training: Insufficient cybersecurity training and awareness among employees can facilitate phishing attacks and internal breaches. Weak password policies (e.g., CWE-521) and communication gaps within organizations heighten the risk of data leaks and spoofing attacks.
* Cloud and edge computing vulnerabilities: Transitioning to cloud services brings forth vulnerabilities in edge computing and cloud architectures. Application programming interface (API) with insecure default configurations can inadvertently expose critical databases to the public internet, as highlighted by CWE-648, indicating the incorrect use of privileged APIs.
# Methods of Analyzing Microsized Plastics in the Environment
## 5 Emerging Technologies
### Atomic Force Microscopy (AFM)-IR, AFM-Raman  
AFM combined with IR or Raman spectroscopy is a potential candidate for the analysis of nanoplastics. AFM can provide images with nanometer resolution, and AFM probes can operate in both contact and non-contact modes with objects. Furthermore, IR or Raman spectroscopy combined with AFM can reveal the chemical structure of the target plastic [71]. The thermal expansion of the sample due to IR absorption causes the AFM cantilever to vibrate, such that the ring-down pattern is analyzed by Fourier transform to extract the frequency and amplitude of the vibration [72]. Moreover, AFM-IR can simultaneously obtain an image with a spatial resolution of 50-100 nm and the IR absorption spectrum of the target. In previous studies, AFM and IR spectra of 100 nm PS beads were successfully acquired. However, finding a single nanoscale plastic particle target to focus on with AFM-IR in an unknown sample is difficult and time consuming.  
One of the time-consuming analytical steps in conventional microplastic analysis methods is the manual search for plastic particles. The time required for this process depends on the efficiency of removing inorganic and organic particles in the separation and purification steps in the pretreatment. Manual identification can miss hard-to-recognize plastic particles, especially small, transparent particles. Automated FTIR/Raman mapping or particle tracking using Raman spectroscopy can address these issues; however, it requires expensive instruments that not all microplastics research laboratories can afford.
## 5 Results and Discussion
### On the Statistical Validation of the Models  
The evaluation results of the three scenarios using the MAE, RMSE, and R2 score metrics, accompanied by their respective F-statistic and \(p\)-value values, are shown in Table 7. In Scenario 1, it is observed that the F-statistic for MAE is 11.43 with a \(p\)-value of 3.23 \(\times\) 10\({}^{-5}\), indicating a significant difference in the mean absolute errors between the models evaluated. Similarly, for RMSE and R2 score, the F-statistic values are 14.50 and 14.13 with \(p\)-values of 2.75 \(\times\) 10\({}^{-6}\) and 3.67 \(\times\) 10\({}^{-6}\), respectively, suggesting that the differences in the mean square errors and coefficients of determination between the models are highly significant.  
In Scenario 2, the results show an F-statistic of 13.18 for MAE. The values for RMSE and R2 score are 9.02 and 8.15, with \(p\)-values less than 0.05 for all. These results indicate significant differences in the error metrics and explanatory power of the models evaluated.  
Finally, in Scenario 3, the F-statistic values for MAE, RMSE, and R2 score are 7.03, 8.87, and 7.42, with \(p\)-values also below 0.05. Although these values are lower compared to the first two scenarios, they are still statistically significant, implying that the observed differences in the error metrics and the coefficients of determination between the models are not attributable to chance.  
In summary, the F-statistic values and \(p\)-values in all scenarios and for all metrics indicate that the observed differences between the models are highly statistically significant. This suggests that the evaluated models perform significantly differently in terms of MAE, RMSE, and R2 score, which is crucial for selecting the most appropriate model for each specific scenario. The \(p\)-value also indicates that the improvements in the performance achieved by the proposed models are highly statistically significant.
# Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation
## 3 The Proposed Technique
### Final Quality Score  
The textural and structural scores of the synthesized image computed using Equations (5) and (10), respectively, are combined to compute the overall quality score \(Q\).  
\[Q=\alpha T+(1-\alpha)S. \tag{11}\]  
The parameter \(\alpha\) is used to adjust the relative importance of textural and structural scores. Its value is empirically set to 0.7. Figure 2 shows the results of the proposed metric on a few sample images from the testing dataset. In a stereoscopic environment, the quality scores obtained by the proposed metric for both views are averaged to get a single quality score. Figure 2b-e are images obtained by DIBR synthesis from the two source color and depth images, the source view images were artificially degraded by introducing additive white noise (AWN) at four different levels, with noise control parameters 5, 17, 33, and 53, respectively. Figure 2a shows the corresponding ground truth image. Below each image, the scores estimated by the proposed metric and the respective subjective scores are reported. It can be noted that the visual quality of the synthesized images degrades as the noise in the source color and depth images increases and our metric effectively captures this quality degradation.  
Figure 2: Results of the proposed quality assessment metric on sample images from the test dataset. (**a**) Original reference image, (**b–e**) are the images generated using DIBR from color and depth images polluted with additive white noise (AWN) at four different levels, with noise control parameters 5, 17, 33, and 53, respectively. The objective quality scores estimated by the proposed method and the subjective scores of these images are shown below them.
## 3 NDN Enabled Communication and Transport Research in V2X
### Wireless Communication in NDN  
[PERSON] et al. [43] provide an outlook on the advance of research in wireless communication for V2X via ICN. [PERSON] et al. [7] studied the applicability of NDN for Vehicular Ad-hoc Networks (VANET) and proposed extensions to NDN for vehicular networks. In particular, they showed that the inherent caching capability of NDN provides a benefit when retrieving data from content providers. They observed that, for the investigated scenario, it is possible to offload huge portions of the data on the mobile nodes (cars). Therefore, the load on the bottleneck from content providers to wireless infrastructure is decreased and the Round-Trip-Time for data packets at vehicles is reduced. [PERSON] et al. discuss a possible implementation of NDN for vehicular network (V-NDN) in [14] and [PERSON] and [PERSON] [44] its enhancement (EV-NDN).  
[PERSON] et al. [45; 46] introduced content-centric networking (CCN) on top of IEEE 802.11p [47] and evaluated its performance in terms of consumers successfully retrieving content bytes,downloading time, and content delivery duty in terms of task distribution of transmitting and forwarding among the Relay Station Unit (RSU) and present vehicles. The results show that the inherent in-network caching of CCN provides a tremendous benefit over the legacy solution. In [48] the signalling part of NDN is also moved to cellular networks and its performance is investigated by simulation of a urban scenario with different traffic density.  
An interesting report of a life-size Internet of Things (IoT) NDN deployment with devices spread over several rooms and floors is provided in [49]. In addition to the experiment description (and the data gathered) the authors also propose enhancements leading to a decreased energy consumption and routing state of their NDN IoT deployment. An interesting review of open challenges of ICN in IoT is provided by [50]. The potential of ICN in wireless sensor and actor networks (WSANs) is investigated in [51]. More specifically, [PERSON] et al. [51] investigate how ICN is able to improve the performance of WSANs with respect to efficient coordination, service discovery and traffic engineering (prioritized routing).  
The advantages of NDN for applications, such as the delivery of multimedia, in particular of dynamic adaptive streaming of multimedia, are investigated in [52]. An information-centric cost-efficient optimization for the delivery of multimedia content for vehicular networks is presented in [53]. The authors model the content mobility and supply demand balance. These are used to formulate a mixed integer problem that aims on minimizing the economic cost for guaranteeing the quality level of multimedia services. A dissemination protocol that resembles ICN/NDN for safety information in vehicular ad-hoc networks is presented in [54]. The proposed information-centric vehicular networking (ICVN) is compared against the conventional approach using 802.11p [47]. Another architecture for enabling vehicle-to-vehicle communication using NDN is presented in [55].
# Response Statistics of a Shape Memory Alloy Oscillator with Random Excitation
## 3 Theoretical Analysis of the SMA Oscillator Model
\begin{array}{c}D_{1}a_{1}=\frac{1}{2\Omega}f\phi_{1}\cos\phi_{0}- \frac{1}{2}\zeta a_{1},\\ D_{1}\phi_{1}=\overline{h}D_{1}W(T_{1})-\frac{3}{4\Omega}f\delta a_{0}a_{1}- \frac{5}{4\Omega}\gamma a_{0}^{3}a_{1}\\ -\frac{1}{2\Omega a_{0}}f\phi_{1}\sin\phi_{0}-\frac{1}{2\Omega a_{0}}f\mu_{1} \cos\phi_{0}.\end{array} \right. \tag{33}\]  
We transformed Equation (33) into Ito stochastic differential equation as follows:  
\[\left\{ \begin{array}{c}da_{1}=(\frac{1}{2\Omega}f\phi_{1}\cos\phi_{0}-\frac {1}{2}\zeta a_{1})dT_{1},\\ d\phi_{1}=(-\frac{3}{4\Omega}f\delta a_{0}a_{1}-\frac{5}{4\Omega}\gamma a^{3}a _{1}-\frac{1}{2\Omega a_{0}}f\phi_{1}\sin\phi_{0}\\ -\frac{1}{2\Omega a_{0}}f\alpha\cos\phi_{0})dT_{1}+h\omega f(T_{1}).\end{array} \right. \tag{34}\]  
Under the steady-state situation, the moment equations should satisfy  
\[\frac{dE(a_{1})}{dT_{1}}=\frac{dE(\phi_{1})}{dT_{1}}=\frac{dE(a_{1}^{2})}{dT_{ 1}}=\frac{dE(\phi_{1}^{2})}{dT_{1}}=\frac{dE(a_{1}\phi_{1})}{dT_{1}}=0. \tag{35}\]Here, \(E(\cdot)\) is the mathematical expectation operator. Then, first- and second-order steady-state moments of the amplitude \(a_{1}\) can be obtained as follows:  
\[E(a_{1})=0,E\Big{(}a_{1}^{2}\Big{)}=\frac{\overline{h}^{2}M_{2}^{2}}{M_{1}M_{2} N_{1}+M_{1}N_{2}^{2}+M_{1}^{2}N_{2}+N_{1}N_{2}M_{2}}, \tag{36}\]  
where  
\[ \begin{array}{c}M_{1}=\zeta,\ M_{2}=\frac{1}{\Omega}f\cos\phi_{0},\\ N_{1}=\frac{3}{2\Omega}\beta a_{0}+\frac{5}{2\Omega}\gamma a_{0}^{3}+\frac{1}{ \Omega a_{0}^{2}}f\cos\phi_{0},\ N_{2}=\frac{1}{\Omega a_{0}}f\sin\phi_{0}. \end{array}  \tag{37}\]  
Thus, the first- and second-order steady-state moments of the steady-state solution of Equation (28) are deduced to  
\[\left\{ \begin{array}{c}E(a)=a_{0}+E(a_{1})=a_{0},\\ E\big{(}a^{2}\big{)}=a_{0}^{2}+E\big{(}a_{1}^{2}\big{)}=a_{0}^{2}+\frac{ \overline{h}^{2}M_{2}^{2}}{M_{1}M_{2}N_{1}+M_{1}N_{2}^{2}+M_{1}^{2}N_{2}+N_{1} N_{2}M_{2}}.\end{array} \right. \tag{38}\]  
According to the literature [16], we can find that if \(0<\theta\leq 1\), martensite is stable; if \(1<\theta<\theta_{A}\), both phases exist; if \(\theta>\theta_{A}\), austenite is stable. The unique mechanical properties of SMA are derived from the phase transition, which can be induced by the change of temperature. Therefore, the temperature \(\theta\) is an extremely important parameter for the SMA systems. In the next section, effects of the temperature \(\theta\) are examined in detail on the nonlinear behaviors of the proposed SMA oscillator model.
## 3 Results
### Effect of CCA Extract on Insulin Resistance in LPS-Induced 3T3-L1 Adipocytes
#### 3.6.1 Effect of CCA Extract on Glucose Uptake in LPS-Induced 3T3-L1 Adipocytes  
As shown in Figure 5, the cellular glucose uptake was decreased in LPS-treated 3T3-L1 adipocytes by 19.4% relative to the untreated control. Interestingly, the CCA extract significantly improved glucose uptake, indicating an anti-insulin resistance effect in LPS-treated adipocytes. The glucose uptakes of cells treated with the CCA extract at the concentrations of 100, 200, and 400 \(\mathrm{\SIUnitSymbolMicro g}\)/mL were significantly increased by 29.3%, 27.3%, and 38.5%, respectively, when compared with LPS-treated cells.
# Electric Stimulation of Astaxanthin Biosynthesis in _Haematococcus pluvialis_
## Materials and Methods  
### Microalga and Photosynthetic Cultivation  
_H. pluvialis_ NIES-144, provided by the National Institute of Environmental Studies (NIES, University of Tokyo, Tokyo, Japan), was used in this study. The NIES-C medium with the following composition (per liter) was used: 0.15 g Ca(NO\({}_{3}\))\({}_{2}\), 0.10 g KNO\({}_{3}\), 0.05 g \(\beta\)-glycerophosphoric acid disodium salt pentahydrate, 0.04 g MgSO\({}_{4}\cdot\)7H\({}_{2}\)O, 0.50 g trisaminomethane, 0.01 mg thiamine, 0.10 \(\mu\)g biotin, 0.10 \(\mu\) vitamin B\({}_{12}\), and 3.0 mL PIV metal solution [21]. One liter of the PIV metal solution contains 1.0 g Na\({}_{2}\)EDTA, 0.196 g FeCl\({}_{3}\cdot\)6H\({}_{2}\)O, 36.0 mg MnCl\({}_{2}\cdot\)4H\({}_{2}\)O, 22.0 mg ZnSO\({}_{4}\cdot\)7H\({}_{2}\)O, 4.0 mg CoCl\({}_{2}\cdot\)6H\({}_{2}\)O, and 2.5 mg Na\({}_{2}\)MoO\({}_{4}\cdot\)2H\({}_{2}\)O. After adjusting the pH to 7.5, the medium was filter-sterilized using a 0.2 \(\mu\)m membrane (Satorius Stedium Biotech., Gottingen, Germany). For the seed culture, one colony was collected from an agar plate culture of _H. pluvialis_ and inoculated in the medium in a 250 mL-scale [PERSON] flask with a porous silicon stopper (working volume, 100 mL) in a shaking incubator (IS-971 RF, Lab Companion, Daejeon, Korea; 25 \({}^{\circ}\)C and 150 rpm) under continuous illumination (light intensity, 40 \(\mu\)mol photons m\({}^{-2}\) s\({}^{-1}\)) [15].  
A primary photoautotrophic culture of _H. pluvialis_ was established using a 250 mL-scale funnel-type photobioreactor (f-PBR; working vol., 150 mL) using a method optimized previously [22]. _H. pluvialis_ cyst cells collected from the seed flask culture were inoculated in the medium contained in the f-PBR till an optical density (OD) of 0.05 was achieved at 660 nm, as measured using a UV-VIS spectrophotometer (Optizen 2120 UV, Mecasys Co., Daejeon, Korea). The f-PBR was continuously supplied with 5% (\(p/v\)) CO\({}_{2}\) in air at a rate of 60 mL min\({}^{-1}\) (0.4 vvm) from the bottom of the reactor. The f-PBR was incubated for 25 days in a plant growth chamber (GC-300, JEIO TECH, Korea; light intensity, 50 \(\mu\)mol photons m\({}^{-2}\) s\({}^{-1}\); 12 h light/dark cycle; 25 \({}^{\circ}\)C). The details of the culture conditions employed have been reported in a previous study [22].
## 3 Image Processing Approaches Used for EAT and PAT Segmentation
### Traditional Image Processing Techniques  
For this review, we consider techniques like thresholding, region growing, active contours, morphological operations, and other image processing techniques that don't include a learning component as traditional image processing methods. These techniques are often combined with using different heuristics to narrow down the search region, perform a rough initialization or guide more complex methods. Heuristics are \"rules of thumb\", relatively simple assumptions based on known examples and experience. For instance, a heuristic is that the femur can be found on an X-ray image as the longest component in the bone intensity range.
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 3 Power Distribution Network Reconfiguration Mathematical Formulation
### Constraints  
In the study of distribution network reconstruction, in order to achieve reasonable optimization and improvement of the distribution network, a series of constraints needs to be considered.  
* Power Flow ConstraintsThe power flow between loads and power sources in a distribution network is realized through components such as cables, transformers, and switches. The power flow constraint controls the tidal current distribution and power balance of the whole system by considering the electrical parameters of the equipment and components in the power system, which can be expressed as:  
\[\left\{\begin{array}{l}P_{i}+P_{DGi}=P_{Li}+U_{i}\sum\limits_{i=1}^{N}U_{j}(G_{ ij}\cos\theta_{ij}+B_{ij}\sin\theta_{ij})\\ Q_{i}+Q_{DGi}=Q_{Li}+U_{i}\sum\limits_{i=1}^{N}U_{j}(G_{ij}\sin\theta_{ij}+B_{ ij}cos\theta_{ij})\end{array}\right. \tag{13}\]  
where \(P_{i}\) and \(Q_{i}\) represent the active and reactive power flowing to node \(i\). \(P_{DGi}\) and \(Q_{DGi}\) denote the active and reactive output of DG at node \(i\). \(P_{Li}\) and \(Q_{Li}\) denote the active and reactive power of the load at node \(i\). \(G_{ij}\), \(B_{ij}\), and \(\theta_{ij}\) represent the conductance, susceptance, and voltage phase difference of branch \(ij\), respectively.  
* Voltage Constraints The voltage constraint means that the amplitude of voltage at each node should be limited to a certain range. This constraint can be expressed as: \[U_{\min}\leq U_{i}\leq U_{\max}\] (14) where \(U_{i,max}\) and \(U_{i,min}\) are the upper and lower limits of the voltage \(U_{i}\) at node \(i\), respectively.
* Current Constraints The current carrying capacity of the cable in the system should not exceed the steady-state thresholds as depicted in Equation (15): \[I_{ij}\leq I_{ij}^{\max}\] (15) where \(I_{ij}\) is the current transmitted through the branch \(ij\), and \(I_{ij,max}\) represents the maximum current limit in the branch \(ij\).
* DG output constraint The current carrying capacity of the cable in the system should not exceed the steady-state thresholds as depicted in Equation (16) \[P_{DG}^{\min}\leq P_{DG}\leq P_{DG}^{\max}\] (16) where \(P_{DG}^{\min}\) and \(P_{DG}^{\max}\) are the upper and lower limits of \(DG\), respectively.
* Topological constraints Topological constraints include radial constraint and connectivity constraint: 1. Radial constraint: The power supply structure of the distribution network must be radial.
2. Connectivity constraint: There must be no islands in the reconfiguration solution, and all nodes must be in a connected state.