esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing BiLSTM Network Attack Prediction Based on Improved Gray Wolf Algorithm
## 4 Experimental Simulation and Analysis
### Discussion  
The rapid development of networks leads to higher requirements for network security. The current academic research on network attack focuses on the analysis of network security event logs detected by network security facilities. In addition, the network attack prediction technology is the most important. Therefore, this paper proposes IGWO-BiLSTM for network attack prediction. As an intelligent optimization algorithm, IGWO can solve engineering problems, optimization problems and applications in neural networks. Meanwhile, compared with GWO and particle swarm optimization algorithms, IGWO has the advantages of global search and has corresponding research value.  
IGWO-BiLSTM used ec_data and DARPA99 to verify the prediction effect. Firstly, compared with BiLSTM, IGWO-BiLSTM greatly shorten the time of network traffic prediction. In practical application, it can provide faster response decisions and effectively reduce the damage to network equipment. Secondly, this model can converge earlier to lower Loss than other models. Loss is defined by MAE, which represents the difference between the predicted value and the true value. Compared with IGWO-RNN, IGWO-LSTM and IGWO-GRU, IGWO-BiLSTM decreased by 0.0033, 0.0013 and 0.00255, respectively. Finally, compared with IGWO-RNN, IGWO-LSTM and IGWO-GRU, the accuracy of IGWO-BiLSTM is improved by 0.33, 0.57 and 0.06, respectively.
## 3 Materials and Methods
### Antioxidant Properties  
#### 3.5.1 DPPH Radical Scavenging Assay  
To evaluate the antioxidant properties of H-CON, H-CBD, H-TER, and H-CBD-TER hydrogels, a 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging test was performed according to the methodology described by [PERSON] et al. [83] with minor modifications. The test was performed on 96-well plates, where 50 \(\mu\)L of individual dilutions of the analyzed hydrogels (in the concentration range from 0.1-5.0%) in distilled water was added to each well. Then, 150 \(\mu\)L of DPPH working solution in methanol (4 mM) was added to the wells. The samples were mixed thoroughly, and the absorbance at \(\lambda\) = 516 nm was measured with a FilterMax F5 microplate reader (Thermo Fisher Scientific, Waltham, MA, USA). Measurements were taken every 5 min for 30 min. All samples were analyzed in triplicate. Distilled water mixed with a DPPH working solution was used as a control. Measurements were performed in triplicate for each test sample. The DPPH radical scavenging activity was calculated using the following equation:  
\[\%\ \text{DPPH scavenging}=\frac{\text{Ac}-\text{As}}{\text{Ac}}\times 100\% \tag{6}\]  
where the following definitions hold:  
Ac is the absorbance of the control sample;  
As is the absorbance of the test sample.
# Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks
## 5 Simulation Setup and Experimental Results
### Overall Performance of EAR  
Based on the experimental results, we show the significance and improvement of EAR approach and comparison of other approaches in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Improvement in EAR** \\ \hline \hline
**Number of alive Days (Scenario-1)** & 0.5–11\% \\ \hline
**Number of alive Days (Scenario-2)** & 7.93–20.37\% \\ \hline
**Number of alive Days (Scenario-3)** & 14.9–27.71\% \\ \hline \multirow{2}{*}{**Residual Energy in J at 9, 18, \& 27 (Events)**} & 9 \\ \cline{2-3}  & 2–15\% & 16–29\% & 16–33\% \\ \hline \multirow{2}{*}{**Actor Recovery time (Seconds) with**} & \multirow{2}{*}{3.19–20\%} \\
1200 \(\times\) **1200 m\({}^{2}\) network topology**} & & \\ \hline \multirow{2}{*}{**Actor Recovery time (Seconds) with 1400**} & \multirow{2}{*}{3.21–20.8\%} \\ \(\times\) **1400 m\({}^{2}\) network topology**} & & \\ \hline \multirow{2}{*}{**Data Recovery of 15 KB with 10 Events**} & \multirow{2}{*}{10–26\%} \\ \cline{2-3}  & & 2.41–20.66\% \\ \hline \multirow{2}{*}{**Execution Time (Seconds)**} & \multirow{2}{*}{0.2–2.9\%} \\ \cline{2-3}  & & \\ \end{tabular}
\end{table}
Table 6: Improvement of EAR in percentile as compared with competing approaches: RNF, DPCRA, ACR, and ACRA.  
Figure 16: Big-O Complexity Chart.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Approaches** & **Excellent 0** & **Description** \\ \hline
**EAR** & O(1) & Excellent \\
**RNF**[10] & O(log \(n\)) & Good \\
**ACRA**[13] & O(\(n\) log (\(n\))) & Bad \\
**ACR**[7] & O(\(n\)) & Fair \\
**DPCRA**[11] & O(2\(n\)) & Worst \\ \hline \hline \end{tabular}
\end{table}
Table 5: Time Complexity of EAR, RNF, DPCRA, ACR, and ACRA using O Big operation.
## 1 Introduction
This approach also incurs significant expenses due to the use of laboratory-fabricated materials and structures, which may exhibit different or fewer manufacturing defects compared to those deployed in the field. The physical finite element model, adaptable to various conditions, offers high efficiency and broad applicability. However, its effective application necessitates prior knowledge of anticipated damage mechanisms. Developing procedures for constructing physical models that accurately represent actual blade damage mechanisms and fatigue loading conditions is thus essential for effective damage mechanism analysis [19]. The assessment of lifespan reduction in wind turbine blade adhesive joints necessitates determining the composite interlaminar fracture toughness, as well as the fracture toughness based on the fatigue crack growth rate, at the specimen level. The dynamic properties of this adhesive model were characterized through tests targeting the critical displacement point in mode I (opening). For crack growth analysis, the fracture energy rate, derived from the fatigue crack growth velocity in mode I tests, was calculated. Furthermore, the virtual crack closure technique (VCCT), integrated with the extended finite element method (XFEM), was utilized to interpret fatigue crack growth characteristics in blade adhesive joints. The validity of this analytical modeling technique was affirmed by comparing and analyzing its results with experimental data. Employing the XFEM-VCCT-based fatigue crack growth modeling approach, the crack growth life of blade-level adhesive joints under fatigue loading was analyzed, focusing on constant amplitude load conditions.
# Town-Watching Workshop Using Disaster Information Tweeting and Mapping System +
## 2 Disaster Information Tweeting/Mapping System
### Disaster Information Mapping System (DIMS)  
As shown in Figure 4, DIMS helps display the map of tweets posted via DITS. The DIMS comprises the following features:  
* DIMS displays a map of the most recent 30 tweets posted using DITS within 20 km of the user's location.
* The map is centered on the user's current position.
* Depending on whether the rescue hashtag and an image are attached, the shape and color of the icon indicate the position of the tweet change.  
Figure 4: Screenshot of DIMS.  
Figure 3: An example tweet posted using DITS.  
DITS/DIMS is a system used for sharing disaster-related information such as damage-related situations and emergency requirements of victims affected by a disaster. However, in the town-watching workshops, this system is used to post and share the information in terms of disaster prevention and mitigation.
## 2 Reliability Analysis Method
### ISSA-Kriging-MCS Model
#### 2.2.4 Sampling Method  
In order to establish an accurate Kriging surrogate model, it is necessary to select sufficient design points through a sampling method. Latin hypercube sampling (LHS) is an efficient stratified sampling method. It divides the range of each variable into several equal probability intervals and then randomly selects a sample point in each interval. In this way, it is possible to obtain a balanced sample distribution throughout the entire design space. This sampling method is especially good at dealing with high-dimensional problems and is suitable for situations with complex sample types and significant feature differences. The sampling process of the LHS method can be summarized as three steps: stratification, sampling, and reordering. A schematic diagram of the sampling process is shown in Figure 1.
## 2 Mathematical Formulation
### Set of Constraints  
The constraints of the problem regarding the sizing and location of D-STATCOMs in distribution networks are related to the main operating limits of the system, such as the balance of reactive and active power, the voltage control limits, and the power limits that can be provided by D-STATCOMs, among others. Equations (4)-(8) define the constraints of the problem studied in this article.  
\[P_{kh}^{g}-P_{kh}^{d}=\sum_{k\in\mathcal{N}}\sum_{m\in\mathcal{N}}Y_{km}V_{kh }V_{mhl}\cos(\delta_{kh}-\delta_{mh}-\theta_{km}),\{\forall k\in\mathcal{N}, h\in\mathcal{H}\}, \tag{4}\]  
\[Q_{kh}^{g}+Q_{k}^{\text{DS}}-Q_{kh}^{d}=\sum_{k\in\mathcal{N}}\sum_{m\in \mathcal{N}}Y_{km}V_{kk}V_{mhl}\sin(\delta_{kh}-\delta_{mh}-\theta_{km}),\{ \forall k\in\mathcal{N},h\in\mathcal{H}\}, \tag{5}\]  
\[V_{min}\leq V_{kh}\leq V_{max},\{\forall k\in\mathcal{N},h\in\mathcal{H}\}, \tag{6}\]  
\[Z_{k}Q_{min}^{\text{DS}}\leq Q_{k}^{\text{DS}}\leq Z_{k}Q_{max}^{\text{DS}},\{ \forall k\in\mathcal{N}\}, \tag{7}\]  
\[\sum_{k\in\mathbb{N}}Z_{k}\leq N_{A}^{\text{DS}}, \tag{8}\]  
where \(P_{kh}^{g}\) is the active power injected in period \(h\) by a standard generator connected to node \(k\); \(Q_{kh}^{g}\) is the reactive power injected by a conventional generator connected to node \(k\) in period \(h\); \(P_{kh}^{d}\) is the active power demanded during period \(h\) by the loads connected to node \(k\); \(Q_{kh}^{d}\) is the reactive power demanded during period \(h\) by the loads connected to node \(k\); \(Q_{k}^{\text{DS}}\) is the reactive power generated by the D-STATCOM at node \(k\); \(V_{min}\) is the minimum voltage at the system nodes; and \(V_{max}\) is the maximum voltage at the system nodes. A binary variable is defined for whether a D-STATCOM is connected at node \(k\), \(Z_{k}\) (\(Z_{k}=1\)) or not (\(Z_{k}=0\)). Finally, \(N_{A}^{\text{DS}}\) is the number of D-STATCOMs available for installation.
## 1 Introduction
### Performance of Pavement Markings' Retroreflectivity  
Pavement markings present retroreflectivity due to the glass beads applied on their surface. The characteristics of the glass beads have a great influence on the retroreflectivity levels. [PERSON] et al. [8] assessed the size distribution (gradation), color, shape, and air inclusion of glass beads and evaluated the influence of these properties on the initial retroreflectivity of laboratory and field samples. The authors evaluated 30 glass bead samples and could not define a definitive relationship. However, the general trends observed showed that samples with higher percentages of round and larger particles, clearer beads, and low air inclusion tended to increase the initial retroreflectivity value.  
Pavement markings' retroreflectivity decreases over time. Frequent snow removal activities, traffic, and dirt accumulation scratch the glass beads' surface, which accelerates the degradation rate of retroreflectivity due to the loss of a polished surface [9]. Moreover, retroreflectivity degradation also occurs due to the loss of glass beads and dirt accumulation on the pavement markings' surface, which reduces the reflectorized area [10]. The glass beads' loss depends on the marking material used; the selection of the type of binder must consider costs and performance [11]. In addition, materials' characteristics are evaluated by laboratory tests to guarantee their quality prior to application.  
Retoreflectivity values and their rates of decrease depend on the traffic volume and composition, as well as climatic conditions such as rain, solar radiation, and temperature. Due to the difficulty of reproducing traffic and weather characteristics in the laboratory, pavement markings' performance are usually evaluated by field tests [12; 13; 14; 15; 16; 17; 18; 19; 20]. Experimental test sites are expensive and require long periods to produce results, but they are necessary for the proper evaluation of materials' performance.
# Application of MMC-RPC in High-Speed Railway Traction Power Supply System Based on Energy Storage
## 4 ES Type MMC-RPC  
In order to utilize of regenerative braking energy in the high-speed railway system and improve the economy and stability of the system. ES type MMC-RPC has been applied, where SC units are distributed connected with submodules of the MMC-RPC, and they are controlled by the VDCM control.  
As shown in Figure 14, the SC-type ES units are connected in parallel to the DC-link capacitors of the submodules through DC/DC converters. The power flow among the DC links of the submodules. In the process of locomotive braking, the ES units absorb the regenerative braking energy, reducing its impact on the traction power supply system. During traction condition, SCs are discharged, thereby reducing the power supply requirement of the system.  
The mechanical equation of the DC motor is [22]:  
\[\left\{\begin{array}{l}\frac{\mathrm{d}\omega}{\mathrm{d}t}=T_{\mathrm{m}}- T_{\mathrm{e}}-D\Delta\omega\\ \frac{P}{\omega}=T\end{array}\right., \tag{24}\]  
where  
1. \(J\) is the moment of inertia of VDCM;
2. \(T_{\mathrm{m}}\) and \(T_{\mathrm{e}}\) are the mechanical torque and electromagnetic torque of VDCM, respectively;
3. \(D\) is the damping coefficient.  
The electrodynamic force balance equation of the armature circuit is:  
\[\left\{\begin{array}{l}L_{\mathrm{a}}\frac{dI_{\mathrm{a}}}{dt}=E-R_{a}I_{a }-U\\ E=C_{T}\Phi\omega\end{array}\right., \tag{25}\]  
where  
1. \(\Phi\) is the torque coefficient of the DC motor;
2. \(C_{\mathrm{T}}\) is the magnetic flux of each pole of the DC motor;
3. \(\omega\) is the angular speed of the DC motor.  
The control method of the energy storage system is shown in Figure 15:  
Figure 14: MMC-RPC scheme based on distributed super capacitor energy storage.  
Figure 15: Control block diagram of VDCM.  
By adjusting the input and output of the DC/DC converter, VDCM makes the converter have the characteristics of DC motor. The VDCM control block diagram is shown in Figure 15. First, take the expected power \(P\) as the mechanical power \(P_{\text{m}}\) and the output power as the electromagnetic power \(P_{\text{e}}\). In the VDCM link, the inertia and damping characteristics of the DC motor are simulated according to the mechanical rotation Equation (24) and the armature loop electromotive force balance Equation (25). The duty cycle of the armature current \(I_{\text{a}}\) is obtained through the current inner loop, and finally the control signal of the switch is obtained through PWM modulation.
## 1 Introduction  
Numerous studies have already been conducted regarding the everyday exposure to environmental radiofrequency (RF) electromagnetic fields (EMF) [1]. However, the advent of the fifth generation (5G) of wireless communications technologies, including the new radio (NR) radio access technology [2] and the widespread use of massive multiple-input-multiple-output (MaMIMO) and advanced antenna systems (AAS) as well as the opening up of new frequency bands, brings about questions from the general public on their additional contributions to the environmental RF exposure. Therefore, accurate characterization of the impact of the ongoing roll-out of 5G NR networks on our exposure to RF-EMF is essential, in particular for the communication of scientific [3; 4; 5] and legislative bodies [6] to the general public.  
Several studies on the experimental assessment of the exposure due to NR base stations have been published in the past few years [7; 8; 9; 10; 11; 12; 13; 14; 15]. Furthermore, in [16], drive-test measurements in three NR networks operating in the 3.6 GHz band were performed, collecting samples of the transmit power (Tx) and of the synchronization signal reference signal received power (SS-RSRP) received by a mobile device, while millions of Tx power samples from user equipment were also recorded in two commercial NR networks in [17].  
However, comprehensive data on the impact of a 5G NR network on the environmental RF-EMF exposure are still lacking. This study fills that gap by gathering both time-averaged and (extrapolated) maximum exposure levels, using the in situ measurement methodology for 5G NR MaMIMO base station exposure described in [13], in a commercial 5G NR network in Bern, Switzerland.  
In short, the contributions of this paper are (a) the first assessment of RF-EMF exposure in a commercial NR network, (b) a range of time-averaged and maximum exposure values in a commercial NR network, extrapolated to maximum antenna input powers, and (c) an extrapolation method for which no information is needed from the operator, validated with the actual antenna radiation patterns.
## 5 Discussion  
### Correlation between InSAR Deformation Results and Multiple Factors  
#### 5.1.1 Rainfall Factor  
Figure 9 illustrates the monthly average rainfall in North Sikkim from 2018 to 2022 and the five-year average rainfall. Notably, the rainy season typically begins around May each year, with May to September experiencing the highest monthly rainfall, often exceeding 300 mm. The InSAR analysis in Section 4 indicates that during the monitoring period, deformation patterns obtained from descending images show a significant acceleration at point P1 in May 2023, coinciding with the onset of the rainy season. In April 2022, rainfall  
Figure 8: Further enlarged GF-2 images focused on the landslide area. (**a**) Prior to the landslide; (**b**) after the landslide.  
in North Sikkim significantly surpassed previous Aprils, reaching levels comparable to those usually seen in May, exceeding 400 mm/month. This suggests an earlier onset of the rainy season in 2022 compared to previous years. Concurrently, as depicted in Figure 6b, there was a notable acceleration in deformation at points P1 and P2 in April 2022, occurring relatively earlier compared to the acceleration observed at point P1 in 2023. Moreover, the average monthly rainfall in September and October 2022 was high, both exceeding 400 mm/month, thereby extending the rainy season compared to previous years. The accumulated deformation and duration of accelerated deformation from April to October 2022 at points P1-P4 over three years, spanning from 2021 to 2023, are the highest, likely due to the prolonged rainy season in 2022. Hence, we believe there is a correlation between the landslide area's deformation rate and rainfall patterns.
## 2 Methods
### Characterizations  
**GPC**: Gel permeation chromatography measurements were conducted on an SSI 1510 HPLC (Scientific Systems, Inc. Burbank, California, USA) equipped with a shodex RI-201H differential refractometer and two series PL1117-6830 and PL1117-6800 300 \(\times\) 7.5 mm columns (Agilent Technologies Inc., Santa Clara, California, USA). The system was equilibrated at 50 \({}^{\circ}\)C in DMF with 1.6 mg/mL LiBr, which served as the polymer solvent and eluent, with a flow rate of 1.0 mL/min. Polymer solutions were prepared at a known concentration (ca. 5 mg/mL), and an injection volume of 20 \(\upmu\)L was used. The system was calibrated using polymethyl methacrylate (PMMA) as a standard.  
**1**_H NMR_: 1H NMR spectra were recorded on a Bruker AVIII400 HD (Bruker Corporation, Billerica, Massachusetts, USA) 400 MHz spectrometer with solvent proton resonance as the reference. The spectra were obtained at room temperature with 32 scans. All the synthetic polymers in this work were dissolved in CDCl\({}_{3}\).  
**DLS**: The size measurements were performed using a Malvern ZEN3690 (Malvern Panalytical, Almelo, Netherlands). The size range of particles measured was 0.3-5000 nm, and the electrical conductivity range was 0-200 mS/cm. Samples were dissolved and diluted in good solvent, and measurements were taken at an angle of 90 degrees. Data were analyzed using Zetasizer (8. 01. 4906) software for size distribution.  
**TGA**: Thermogravimetric analysis was performed on SMNCs using a DTG-60H (Shimadzu Corporation, Kyoto, Japan) instrument to determine the silica-to-polymer ratio. Samples were heated from room temperature to 800 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min under a nitrogen atmosphere.  
**SEM**: The morphologies of SMNCs were imaged using a JSM 7610F Scanning Electron Microscope (JEOL Ltd., Tokyo, Japan). Samples for SEM were prepared by casting 1.0 \(\upmu\)L of solution onto silicon wafers and immediately evaporating solvents (THF) under strong dry air blowing within \(\sim\)1 s, with casting repeated twice.  
**TEM**: The morphologies of SMNCs were imaged using a JEM-F200 Transmission Electron Microscope (JEOL Ltd., Tokyo, Japan). TEM samples were prepared by casting 5-10 \(\upmu\)L of NP solution on 300-mesh copper grids covered with carbon film and then dried at room temperature.
## 3 Results
### Trends
#### 3.3.2 Trends of the Aerosol, Tropospheric NO\({}_{2}\) and Clouds Radiative Effect
NO\({}_{2}\)** & **CFL** & **COTL** & **CFI** & **COTI** \\ \hline Antikythera & \(-0.002\)\(\pm\) 0.001 & 0.00 \(\pm\) 0.00 & \(-0.001\)\(\pm\) 0.001 & 0.025 \(\pm\) 0.131 & 0.000 \(\pm\) 0.001 & 0.025 \(\pm\) 0.229 \\  & (\(-1.11\)\(\pm\) 0.35) & (0.09 \(\pm\) 0.32) & (\(-0.53\)\(\pm\) 0.45) & (0.20 \(\pm\) 1.09) & (0.34 \(\pm\) 0.82) & (0.21 \(\pm\) 1.95) \\ Argos & \(-0.003\)\(\pm\) 0.001 & \(-0.03\)\(\pm\) 0.00 & \(-0.003\)\(\pm\) 0.001 & 0.129 \(\pm\) 0.112 & **0.003 \(\pm\) 0.001** & 0.055 \(\pm\) 0.281 \\  & (\(-1.500\)\(\pm\) 0.520) & (\(-1.72\)\(\pm\) 0.27) & (\(-1.3\)\(\pm\) 0.48) & (0.74 \(\pm\) 0.64) & **(2.13 \(\pm\) 0.95)** & (0.32 \(\pm\) 1.64) \\ \cline{2-7} Athens & \(-0.003\)\(\pm\) 0.003 & \(-0.19\)\(\pm\) 0.02 & \(-0.003\)\(\pm\) 0.001 & 0.079 \(\pm\) 0.138 & 0.001 \(\pm\) 0.001 & 0.008 \(\pm\) 0.210 \\  & (\(-1.25\)\(\pm\) 1.24) & (\(-3.57\)\(\pm\) 0.40) & (\(-1.06\)\(\pm\) 0.41) & (0.52 \(\pm\) 0.91) & (0.88 \(\pm\) 0.97) & (0.05 \(\pm\) 1.49) \\ HCMR & 0.000 \(\pm\) 0.001 & \(-0.01\)\(\pm\) 0.00 & \(-0.001\)\(\pm\) 0.001 & 0.069 \(\pm\) 0.101 & 0.001 \(\pm\) 0.001 & 0.0052 \(\pm\) 0.255 \\  & (\(0.12\)\(\pm\) 0.73) & (\(-0.99\)\(\pm\) 0.28) & (\(-0.35\)\(\pm\) 0.52) & (0.48 \(\pm\) 0.69) & (0.54 \(\pm\) 1.00) & (0.40 \(\pm\) 1.94) \\ Ioannina & \(-0.001\)\(\pm\) 0.001 & \(0.00\)\(\pm\) 0.01 & \(-0.003\)\(\pm\) 0.001 & \(-0.102\)\(\pm\) 0.137 & 0.001 \(\pm\) 0.001 & 0.095 \(\pm\) 0.339 \\  & (\(-0.62\)\(\pm\) 0.77) & (\(-0.40\)\(\pm\) 0.54) & (\(-0.83\)\(\pm\) 0.41) & (\(-0.45\)\(\pm\) 0.60) & (0.81 \(\pm\) 0.75) & (0.40 \(\pm\) 1.94) \\ Kozani & \(-0.002\)\(\pm\) 0.001 & \(-0.21\)\(\pm\) 0.03 & \(-0.003\)\(\pm\) 0.002 & \(-0.186\)\(\pm\) 0.161 & 0.000 \(\pm\) 0.001 & \(-0.047\)\(\pm\) 0.328 \\  & (\(-1.03\)\(\pm\) 0.71) & (\(-3.95\)\(\pm\) 0.52) & (\(-0.82\)\(\pm\) 0.48) & (\(-0.83\)\(\pm\) 0 the liquid and ice cloud optical thickness has increased, and for some others it has decreased during the same period, suppressing or boosting the changes in the radiative effect of clouds induced by changes in the liquid and ice cloud fractions. Our results support previous regional and global studies that underlined the importance of cloud optical thickness in the determination of SSR trends and the diming/brightening effect (e.g., [PERSON] et al. [95]; [PERSON] et al. [96]; [PERSON] et al. [97]).