esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation of Inelastic Seismic Analysis
### Analysis Modeling
#### 4.1.3. Inelastic Material Model  
For inelastic seismic analyses, the well-known constitutive equations of [PERSON]'s kinematic hardening model [19; 20] is used as follows:  
\[\dot{\alpha}_{ij} = \sum_{k=1}^{3}\left[\frac{2}{3}C_{k}\dot{\epsilon}_{ij}^{p} - \gamma_{k}\big{(}\alpha_{ij}\big{)}_{k}\dot{p}\right] \tag{6}\]  
where \(\dot{\alpha}_{ij}\) and \(\dot{p}\) indicate the revolution of back stress and an accumulated plastic strain, respectively. Additionally, \(C_{k}\) and \(\gamma_{k}\) (\(k\) = 1\(\sim\)3) are material constants to be used in the ANSYS program.  
For the isotropic hardening model, the inelastic Vocc model [21] is used as follows:  
\[\dot{R} = b\left[Q\ -\ R\right]\dot{p} \tag{7}\]  
where \(\dot{R}\) indicates the revolution of drag stress. Additionally, \(b\) and \(Q\) are material constants.  
Table 9 presents the used material constants required in Equations (6) and (7) for Type 316 stainless steel [22].  
Figure 14: Spectrum illustrating the half-power bandwidth method for damping estimation (1 st EW mode).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{\(\sigma_{yo}\) \(\times\) **10\({}^{6}\) (Pa)**} & E & \multirow{2}{*}{\(C_{\rm I}\) \(\times\) **10\({}^{9}\)**} & \multirow{2}{*}{\(\times\) **10\({}^{9}\)**} & \multirow{2}{*}{\(\times\) **10\({}^{9}\)**} & \multirow{2}{*}{\(\times\) **10\({}^{9}\)**} & \multirow{2}{*}{\(\times\) **10\({}^{3}\)**} & \multirow{2}{*}{\(\times\) **10\({}^{3}\)**} & \multirow{2}{*}{\(\times\) **10\({}^{3}\)**} & \multirow{2}{*}{\(\times\) **10\({}^{3}\)**} & \multirow{2}{*}{\(\times\) **10\({}^{3}\)**} \\  & \(\times\) **10\({}^{6}\) (Pa)** & & & & & & & & \\ \hline Type 316 SS & 135 & 190 & 120 & 20.2 & 10.67 & 1.0 & 1.0 & 1.0 & 45.0 & 85 \\ \hline \end{tabular}
\end{table}
Table 9: Material constants for inelastic material models.
# Cross-Medium Photoacoustic Communications: Challenges, and State of the Art
## 5 PA-Based Communication and Localization
### Current State of PA Communication Research
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wave Shape** & **Laser Energy (J)** & \
\begin{tabular}{c} **Experimental SL** \\ **(dB re 1 μPa)** \\ \end{tabular} &
\begin{tabular}{c} **Experimental Range (M)** \\ \end{tabular} \\ \hline
10-kHz Chopper & 30 & 113 & 100 \\
10-kHz Sinewave & 25 & 111.8 & 78 \\ Pulse Train & 25 & 112.1 & 80 \\
4-Bit PSK & 25 & 106 & 15 \\
11-Tone MFSK & 25 & 101.1 & 23 \\
5-Tone MFSK & 25 & 100.7 & 22 \\
2-Tone MFSK & 25 & 104.4 & 33 \\
2-Tone FSK & 25 & 104.9 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of range and sound pressure level for different modulation techniques using linear photoacoustic [39]. Figure 13: General block diagram of air to underwater photoacoustic communication. plasma is considered as an antenna whose directionality is spherical in shape. On the other hand, an elongated plasma is considered as a non-spherical or highly directional acoustic source. The idea of OFAM is to dynamically control the focal length by using electrically focus-tunable lens. Advanced lenses are available where the focal length of the lens is a function of electrical current [84]. Figure 14 shows how we can generate such different shapes of plasma using focus-tunable lenses and a lens driver. Through rigorous simulations and experiments, we have shown that OFAM can achieve better bit error rate (BER) performance. The achievable bit rate using PA mechanism is very low (couple of Hz to kHz). So, sending large data like speech is not possible using PA mechanism. An interesting research work has been done by [PERSON] et al., where a speech signal is transmitted using a PA mechanism [85]. A speech recognition method has been proposed where speech is converted to characters and then encoded by the pulsed laser. The encoding process is done by varying the laser pulse repetition rate. Through experiments, they have shown that converting speech into characters can be reduced the required data rate thus PA mechanism can apply to send speech data. Most of the above works have been done in the lab environment but NRL has successfully done open water demonstration of laser acoustic generation and propagation in 2010 and 2011 at the Lake Glendora Test Facility of Naval Surface Warfare Center, Crane Division [70]. The generated acoustic SL was about 190 dB, and boat-mounted hydrophones measured acoustic pulse propagation at distances up to 300 m (approximately 1000 ft). These field experiments confirmed that there is no significant ultrasonic attenuation at these ranges during acoustic propagation.
# SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots
## 1 Introduction  
Underwater robots are becoming more and more popular to carry out maritime operations, such as oil spill detection [1], bathymetric survey [2], plume tracking [3] and corrosion repair [4]. Underwater robots include heterogeneous vehicles capable of providing different functionalities, such as Autonomous Underwater Vehicles (AUVs), Autonomous Surface Vehicles (ASVs), and Remotely Operated Vehicles (ROVs), to perform maritime and underwater-related tasks. While dealing with complex missions that outmatch the capabilities of a single robotic vehicle (e.g., it is not realistic to use a single ROV for constructing a berm on seabed) or operations that are dangerous for divers, seamless cooperation between different vehicles is demanded to tackle the complexity or riskiness of maritime missions.  
To enable cooperation between a variety of maritime robotic vehicles, namely produced by different manufacturers, thus opening up new applications and ensuring reusability, is one of themain objectives of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) (The SWARMs project: [[http://swarms.eu/](http://swarms.eu/)]([http://swarms.eu/](http://swarms.eu/))) project, a European project in the field of underwater cyber-physical systems (CPS) and robotics. In order to achieve this ambitious goal, the SWARMs project must tackle a set of challenges [5], e.g., defiant maritime and underwater communication and data heterogeneity.
## 3 Results and Analysis
### Comparison of Reflectance among Different Sensors
The percentage density plots of reflectance differences between Landsat-8 OLI and each of ZY-3 MUX, GF-1 WFV and HJ-1 CCD in the red and NIR bands are shown in Figure 11. The majority of reflectance values of ZY-3 MUX and HJ-1 CCD were higher than those of Landsat-8 OLI in both the red and NIR bands (Figure 11a,c). Most of the reflectance values of GF-1 WFV were lower than those of Landsat-8 OLI in the red band and higher than those of Landsat-8 OLI in the NIR band (Figure 11b). However, due to the similar observation geometry conditions between ZY-3 MUX and Landsat-8 OLI, with near nadir observations of Landsat-8 OLI and an SZA of 27.67\({}^{\circ}\), the reflectance differences between ZY-3 MUX and Landsat-8 OLI were lower than those between GF-1 WFV or HJ-1 CCD and Landsat-8 OLI (Figure 11a). In addition, the sensor stability in the NIR band was higher than that in the red band for all three Chinese satellite sensors, with lower reflectance differences from Landsat-8 OLI.
# Issues and Solutions in Deep Learning-Enabled Recommendation Systems within the E-Commerce Field
## 2 Background
### Issues of Recommendation Systems  
The researchers presented alternate solutions to recommendation difficulties such as accuracy, sparsity, and the cold-start problem by exploiting deep learning's efficacy in extracting hidden characteristics and associations. These difficulties are briefly described in this section. The rating's precision forecasts, product estimation, item ranking, and users' review prediction are the most discussed difficulties of recommendation systems. As a result, to improve the preferred ability and efficacy, a recommendation system must meet a certain level of forecast accuracy [1]. The problem of sparsity is unique to collaborative recommendation systems based on similarity [30].
# Optimization Design of Centrifugal Fan Blades Based on Bezier Curve Method
## 3 Multi-Objective Optimization Task
### Optimization Schemes  
The optimization scheme is illustrated in Figure 8. First, parameters were sampled using the optimal Latin hypercube design method, and after modeling and calculations, a sample dataset was established. Then, a radial basis neural network was used to train the dataset, and finally, a multi-objective genetic algorithm was employed for optimization.  
The radial basis function neural network (RBFNN) is a feedforward neural network mainly used for tasks such as function approximation, classification, and pattern recognition. The core idea of the RBFNN is to use radial basis functions as activation functions for hidden layer neurons to construct a nonlinear mapping structure, as shown in Figure 9.  
Figure 8: Optimization schemes of the fan.  
Figure 7: Fitting blade profile using fourth-order Bezier curve.  
Figure 9: RBFNN network structure.  
The activation function can be expressed as  
\[R(x_{p}-c_{i})=exp(-\frac{1}{2\sigma^{2}}\big{\|}x_{p}-c_{i}\big{\|}^{2}),i=1,2, \ldots m \tag{12}\]  
where \(x\) is an n-dimensional input vector; \(c_{i}\) is the center value of the \(i\)-th basis function, which is the same as the input vector; \(\sigma_{i}\) represents the normalized constant of the width of the \(i\)-th center point of the basis function; and \(\big{\|}x_{p}-c_{i}\big{\|}\) is the norm of vector \(x_{p}\)-\(c_{i}\), representing the distance between \(c_{i}\). The Gaussian function value \(R_{i}(x)\) is obtained and is unique at the center value of a certain basis function. According to the above equation, as the \(\big{\|}x_{p}-c_{i}\big{\|}\) increases, the value of the basis function \(R_{i}(x)\) decreases until it approaches 0. For a given input value, a small portion near the center of \(x\) is not activated.  
The samples were divided into two parts, 80% for training and 20% for validation. The prediction results of the radial basis function neural network after training are shown in Figure 10. The absolute static pressure error of the verification points was mostly within 5 Pa, and the maximum absolute static pressure error was 12.5 Pa. The absolute static efficiency error of the verification points was mostly within 0.1%, and the maximum absolute static pressure efficiency error was 0.33%. The determination coefficients for static pressure and static pressure efficiency were 0.98 and 0.96, respectively. The error was within an acceptable range, and the overall model had a high level of confidence. It is believed that the trained neural network model can replace numerical simulation to predict target values and can be used for subsequent research.
# Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System
## 3 Hybrid Multi-Agent Orchestration Algorithm
### Algorithm Design
For instance, when an agent encounters resource shortages, it can request assistance from other agents through information exchange and collaboration, thus preventing task failure. Taking the handling of gas detection events as an example, the central control unit decomposes the alarm event into multiple subtasks and assigns these subtasks to different agents. One agent is responsible for collecting sensor data, another agent analyzes the data to determine whether an alarm should be triggered, and yet another agent generates the alarm report. These agents exchange information such as sensor data, analysis results, and report content, and collaborate to ensure that the tasks are completed smoothly. Without information exchange and collaboration, each agent would have to work independently, leading to inefficiency and a higher likelihood of errors.
## 3 Proposed Strategies
### Ziegler-Nichols Tuning  
We use the relay feedback method [34] to obtain the parameters \(K_{u}\) and \(T_{u}\). Then, we utilize the Z-N tuning method to obtain and set the PID gain parameters \(K_{p}\), \(K_{i}\), and \(K_{d}\), as presented in Table 1. The steps of the relay feedback method are as follows:  
**STEP S1.** In the original system, the PID controller is changed to the relay block, as shown in Figure 4.  
**STEP S2.** The on-off control of the relay block to lead the system generates delay and disturbance, where the on-off control is simulated by +1 and -1, as shown in Figure 5.  
**STEP S3.** The oscillation curve of the system is shown in Figures 6 and 7.  
**STEP S4.**\(T_{u}\) and \(a\) are obtained, as shown in Figures 6 and 7. \(T_{u}\) and \(a\) are substituted into the formula \(K_{u}=\frac{4}{n}\times\frac{d}{a}\) to calculate \(K_{u}\), where \(T_{u}\) is the oscillation period, \(a\) is the amplitude, \(K_{u}\) is the critical gain value, and \(d\) is the output value of Relay (which has been set to 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Controller** & \(K_{p}\) & \(K_{i}\) & \(K_{d}\) \\ \hline P & 0.5 \(K_{u}\) & NA & NA \\ PI & 0.45 \(K_{u}\) & 0.83 \(T_{u}\) & NA \\ PID & 0.6 \(K_{u}\) & 0.5 \(T_{u}\) & 0.125 \(T_{u}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ziegler–Nichols test (Z–N) tuning method.
## 3 Discussion
In this framework, good examples of aromatic plants with interesting potential include _Foeniculum vulgar_ Mill., _Coriandrum sativum_ L., _Mentha longifolia_ (L.) L., _Ocinum basilicum_ L., _Pimpinella anisum_ L., _Thymus_ spp., and _Eucalyptus_ spp. [69; 71; 72]. In addition, the adulticidal activity of this essential oil appeared to be due to both contact and fumigation activity, as shown in Tables 1 and 2. The results of both types of tests show the prospective use of this essential oil as an active insecticidal or fumigant substance suitable for the elimination of adult mosquitoes in closed rooms. Based on the results of tarsal tests and estimated LC\({}_{90}\) of 46.9 \(\upmu\)g cm\({}^{-2}\), it could be estimated that an effective concentration of about 0.5% can be used for contact spraying against mosquito adults. However, further semi-field and field tests are required to verify the effectiveness of our estimated concentration. Similarly, testing the capabilities of encapsulation technology and synergic relationship with other essential oils will help to increase the efficiency and prolong the duration of efficacy of potential botanical insecticides [73; 74]. The good bioactivity of this essential oil can be ascribed to the major compound, carvacrol (82.6%), as showed by its low LC\({}_{50(90)}\) values estimated here against _C. quinquefasciatus_ larvae (29.5 mg L\({}^{-1}\)) and adults (tarsal test: 25.5 \(\upmu\)g cm\({}^{-2}\); fumigation test: 0.8 \(\upmu\)L L\({}^{-1}\) after 24 h of exposure), respectively. However, possible interactions with other minor components (\(p\)-cymene, \(\gamma\)-terpinene, and thymol) might be possible. Carvacrol is a monoterpene phenol considered as a typical marker of oregano. It is widely recognized as a potent antimicrobial and antioxidant agent and therefore used as a food preservative [66]. Together with its isomer thymol, carvacrol is classified as a Generally Recognized as Safe (GRAS) compound by the US Food and Drug Administration (FDA) so that its toxicity on mammals can be regarded as relatively low [75; 76]. Indeed, its LD\({}_{50}\) in rats, after gavage administration, is 810 mg/kg body weight [77]. Furthermore, carvacrol showed negligible effects on beneficial organisms such as mealworm beetles, honeybees, shellfish, and the mosquito fish _Gambusia affinis_ Baird and Girard [78; 79; 80]. In our study, carvacrol proved to be highly effective against L3 larvae of _A. simplex_ showing LD\({}_{50}\) values of 0.176 mg mL\({}^{-1}\) at 24 h and 0.178 mg mL\({}^{-1}\) at 48 h. Furthermore, it inhibited the AChE enzyme as a possible target for its mode of action [31]. In this respect, the interaction with the GABA\({}_{\rm A}\) and octopamine receptors may also be responsible for its toxicity on parasites and pests [81; 82]. In detail, concerning mosquitoes, carvacrol exhibited high toxicity against larvae of different species, including _C. quinquefasciatus_, _Culex tritaciniorhynchus_ [PERSON], _C. pipiens_, _Anopheles stephensi_ [PERSON], and _Anopheles subpictus_ ([PERSON]) with LC\({}_{50}\) values of 26.1, 28.0, 37.6, 21.2, and 24.1 ppm, respectively [83; 52].
###### Abstract  
The plane formed by the intersection of bilateral porions (PoR and PoL) and left orbitale (OrL) is conventionally defined as the Frankfort horizontal (FH) plane. We aim to test the influence of the FH plane definition on a 3D cephalometric assessment. We selected 38 adult patients (20 males, 18 females; average age: 22.87 \(\pm\) 5.17 years) without any gross asymmetry from retrospective records and traced and analyzed their cone-beam computed tomographic images. The findings were categorized into the following four groups: FH1: conventional; FH2: PoR, PoL, right orbitale (OrR); FH3: OrR, OrL, PoL; FH4: OrR, OrL, PoR. The average menton (Me) deviation from the MSP was statistically significant for the FH1 group (0.56 \(\pm\) 0.27 mm; \(p\) \(<\) 0.001), compared to the FH3 (1.37 \(\pm\) 1.23 mm) and FH4 (1.33 \(\pm\) 1.16 mm) groups. The spatial orientation level (SOL) of the FH plane showed a marked difference (\(p\) \(<\) 0.05) between the FH2 (0.602\({}^{\circ}\)\(\pm\) 0.503\({}^{\circ}\)) and FH4 (0.944\({}^{\circ}\)\(\pm\) 0.778\({}^{\circ}\)) groups. The SOL of the MSP was comparatively small (\(p\) \(<\) 0.001) for FH2 (0.015\({}^{\circ}\)\(\pm\) 0.023\({}^{\circ}\)) in comparison to both FH 3 (0.644\({}^{\circ}\)\(\pm\) 0.546\({}^{\circ}\)) and FH 4 (0.627\({}^{\circ}\)\(\pm\) 0.516\({}^{\circ}\)). Therefore, the FH plane definition can significantly influence the interpretation of cephalometric findings. Future studies should focus on standardization to improve the reliability and reproducibility of 3D cephalometry.  
Keywords:FH plane; cone-beam computed tomography; CBCT; facial asymmetry; imaging; three-dimensional +
Footnote †: journal: Applied Sciences
## 3 Proposed Dynamic Graph Processing Scheme
### Processing Loaded Partitions  
After the preprocessed partitions are prioritized by the Scheduling Manager, the Partitions Dispatcher transfers them from host memory to GPU memory. Each partition, optimized for parallel processing, is transferred according to the determined loading order and stored in the GPU's global memory. The Process Manager then ensures that these partitions, loaded via the Partitions Dispatcher, are efficiently processed simultaneously on the SMs. SMs, being the core components of the GPU, are responsible for executing parallel tasks. The Process Manager coordinates tasks within the SMs, ensuring that each operates as efficiently as possible to maximize GPU resource utilization.  
When processing partitions, the computational loads of different tasks may vary, leading to load imbalance. To address this, an SM Switcher is employed to ensure load balancing by evenly distributing workloads across the SMs.  
The SM Switcher continuously monitors the workload of each SM and redistributes unprocessed tasks from heavily loaded SMs to idle ones. This process involves temporarily pausing tasks assigned to overloaded SMs, dividing the unprocessed portions into smaller subsets, and reallocating these subsets to idle SMs. By dynamically balancing workloads, the SM Switcher optimizes resource utilization and enhances the overall efficiency of dynamic graph processing.
# Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea
## 5 Conclusions  
In this study, an improved version of RNN, i.e., LSTM neural network, is trained to predict the water level during the storm surge period in the East China Sea based on the meteorological and water level data of 12 typhoons from 1986 to 2016. The 1 h prediction ability of the LSTM model performs quite well with the MAE, RMSE, and ACC values of 13.40 cm, 16.28 cm, and 0.95, respectively and the maximum difference between the model prediction values and the observation values is 18 cm during the testing set. When extending the prediction time to 3 h, 7 h, and 15 h and comparing their model results with 1 h prediction time, it can be concluded that the LSTM model performs precisely for 1 h prediction of water level during the storm surge period and it can provide a 15 h prediction of water level within a limited error. In addition, four traditional ML models are trained in the prediction times of 1 h, 3 h, 7 h, and 15 h, and the model results are compared with the LSTM model. Based on the comparison analysis, it indicates that the prediction performance of the LSTM model is visibly superior to the four traditional ML models by 27% in terms of ACC. In general, the LSTM model can help engineers and decision-makers to quickly obtain the warning information of the storm surge in advance based on the reasonable water level prediction and immediately make fast emergency responses.  
Conceptualization and methodology, K.C. ([PERSON]), C.K., L.W. and K.C. ([PERSON]); software, K.C., L.W. and K.C.; writing--original draft preparation, K.C. and X.H.; writing--review and editing, C.K. and J.F.; supervision, C.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Innovation Program of Shanghai Municipal Education Commission, grant number 2021-01-07-00-07-E00093, and the National Key Research and Development Project of China, grant number 2019 YFC1407900.  
Not applicable.  
Figure 8: Model evaluation of testing set for (**a**) MAE, (**b**) RMSE, (**c**) ACC, and (**d**) Maximum Difference with 1 h, 3 h, 7 h, and 15 h prediction times.  
**Acknowledgments:** We are grateful to the three anonymous reviewers who provide constructive comments and suggestions, which have greatly helped us to improve the quality of this manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
During ballistic flight, a re-entry vehicle is subjected to high-level structural vibrations due to pressure fluctuations on its bounding surface. The aim of this work is to simulate this structural vibration response. The first step, which is not covered in this study, is the modeling of pressure fluctuations using aerodynamic simulation results. The second step is the simulation of the vibroacoustic response. In this study, the full-frequency vibroacoustic modeling of a metal shell representing a re-entry vehicle aeroshell is developed. The low-frequency response is computed using a FEM-BEM model while Statistical Energy Analysis is used for high-frequency behavior. The validation of these models is based on a ground experiment with controlled diffuse-field acoustic loading. A dedicated reverberation chamber was developed with loudspeaker excitation. The simulation results are compared with the experimental results. In the low-frequency range, simulation helps to understand the measured response spectra by highlighting the acoustic resonances and scattering phenomena. In the high-frequency range, an experimental identification of the damping loss factors and SEA modeling of each subsystem using an FE-SEA approach provides a predictive simulation of the vibration-response spectrum. In this application, FEM-BEM and SEA models are complementary in simulating full-frequency vibroacoustic responses.  
vibroacoustic simulation; diffuse acoustic field testing; FEM; BEM; SEA Article