esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Field Measurement Results and Analysis
#### 4.2.5 Kirchhoff Pre-Stack DEPTH Migration Imaging Results  
By using the comprehensive velocity modeling method, the Kirchhoff pre-stack depth migration imaging processing of seismic data in the study area is completed by using the methods of velocity update and grid tomography. In order to compare the effects of migration imaging, the imaging results of the pre-stack depth migration profile and pre-stack time migration profile are compared and analyzed. It can be seen from Figure 14 that the imaging quality of the pre-stack depth migration section is greatly improved compared with that of pre-stack time migration, which is manifested in the higher signal-to-noise ratio of the target layer, clearer reflection wave characteristics, and better event continuity. On the depth migration profile, the fault distribution, breakpoint location, and contact relationship near the fault near the target layer are obviously clearer, and the diffraction wave homing is more reasonable. In general, Kirchhoff pre-stack depth migration significantly improved the signal-to-noise ratio, resolution, and imaging effects compared with pre-stack time migration, which can be used to further explore the geological structure of the study area.
# SLLB-DEVS: An Approach for DEVS Based Modeling of Semiconductor Lithography Load Balance
## 2 Methodology
### Default DEVS Model Design  
The designed DEVS model in Figure 4 is a model of a single E-beam cluster operation type. We propose an extended DEVS model for a multi-type E-beam work cluster: a new load balancing method for improved management and operation.  
* The generator The generator creates E-beam jobs according to the distribution of jobs generated in the E-beam cluster. The E-beam job includes the attribute of the value of density. The density attribute value follows the Gaussian distribution. The load balancing simulation experiment for the E-beam job applies an empirical model, which requires a higher TAT for higher-density jobs. In order to facilitate the delay analysis in the experiment, each job density attribute should follow a Gaussian distribution.
* The load Balancer The load balancer queues the job and observes the status of the idle server. It then performs the task of allocating the queued task to the appropriate server.
* The server The server performs the actual processing of the incoming job. Processing time follows the Gaussian distribution according to the number of core allocations and the density of the job. When the task is finished and becomes idle, it relays the current status to the load balancer.
* The report The report collects jobs that have finished working, and collects statistical information such as the TAT, the wait time, and the time to job completion in all assigned servers.
## 3 Experimental Results
### Dose Length Product (DLP) Correlations  
The dataset includes DLP values ranging from 16 mGy.cm to 2498 mGy.cm. As shown in Table 4, linear and multiple linear regression models were used to examine the relationship between DLP as an output variable and BMI, weight and age as input variables after excluding up to five outliers.  
As can be seen in Table 4, among the CT exams, the cardiac calcium score exam exhibited a significant (\(p\)-value \(<\) 0.05) strong linear relationship between DLP and BMI. The cardiac angiogram exam showed a significant moderate linear relationship between DLP and BMI, while the other exams showed poor correlations. The patient's weight seems to have a significant (\(p\)-value \(<\) 0.05) relationship with DLP in all CT exams, especially in the cardiac calcium score exam. Similarly, as observed before, the patient's age did not display any linear relationship with DLP across all four CT exams.  
Using multiple regression resulted in a higher correlation with the output variables than when using simple regression in the abdomen/pelvis exam. This enhancement was evident when comparing the R\({}^{2}\), MAPE and \(r\) values of the multiple linear regression models with the highest values among the respective simple linear regression models. The multiple linear regression models in the rest of the CT exams showed correlation values similar to those of the best respective simple linear regression models.
# Improved DRUS 4H-SiC MESFET with High Power Added Efficiency
## Device Structure and Simulation Method and Fabrication Feasibility
### Fabrication Feasibility  
The LDUS-MESFET can be fabricated using the same procedures as reported in [16]. It is worth noting that the doping concentration of \(6\times 10^{17}\) cm\({}^{-3}\) region under the lower gate can be formed by ion implantation and activation process. Through high temperature and multi-energy ion implantation with phosphorous, and activation of the implanted ions can be achieved by inductively heating at a  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline P-Buffer Concentration & \(1.4\times 10^{15}\) cm\({}^{-3}\) \\ N-Channel Concentration & \(3\times 10^{17}\) cm\({}^{-3}\) \\ N-Cap layers Concentration & \(2\times 10^{19}\) cm\({}^{-3}\) \\ Upper layer Concentration & \(6\times 10^{17}\) cm\({}^{-3}\) \\ N-Cap layers Thickness & \(0.2\)\(\mu\)m \\ N-Channel Thickness & \(0.25\)\(\mu\)m \\ P-Buffer Thickness & \(0.5\)\(\mu\)m \\ Recess gate Thickness & \(0.05\)\(\mu\)m \\ Recess gate Width & \(0.35\)\(\mu\)m \\ \(W\) & \(0.3\)\(\mu\)m \\ \(S\) & \(0.2\)\(\mu\)m \\ \(L_{\text{gs}}\) & \(0.5\)\(\mu\)m \\ \(L_{\text{gd}}\) & \(1.0\)\(\mu\)m \\ \(L_{\text{s}}\) & \(0.5\)\(\mu\)m \\ \(L_{\text{d}}\) & \(0.5\)\(\mu\)m \\ \(L_{\text{g}}\) & \(0.7\)\(\mu\)m \\ \(H_{1}\) & \(0.05\)\(\mu\)m \\ \(H_{2}\) & \(0.05\)\(\mu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Common parameters of the two structures  
Figure 2: Comparison of experimental data and simulation data on output current.  
desired time and temperature in an Ar atmosphere [17]. The formation of undoped space region is formed by high energy ion implantation of deep level vanadium and high temperature annealing (the resistivity of the US region is \(2\times 10^{6}\)-\(7.6\times 10^{6}\)\(\Omega\)-cm).  
The recessed gate area of the transistors can be fabricated as reported in [16] as follows: First, a thermal oxide layer is deposited on top of the channel, the thermal oxide layer is etched through the position of the recessed area and continues to be etched into the interior of the channel to form a recessed area having a thickness of 0.05 \(\upmu\)m. Second, Nickel with a work function of 5.1 eV was deposited on the recessed area to form a Schottky contact.
## 2 Materials and Methods
### Stabilization of Range-Gate Position and Doppler Raw Data Acquisition  
In practical measurement conditions, it can be necessary to re-adjust the position of the range gate due to an inadequate position of the transducer or movement of patient. In addition, multiple blood vessels may exist on a single scan  
Figure 8: Hysteresis operation of comparator to generate the _Range gate_ signal.  
Figure 7: Block diagram of range-gate-positioning scheme in FPGA; ADC: analog-to-digital converter; BPF: band-pass filter; FIFO: first-in first-out.  
multiple _Range gate_ pulses. The proposed circuit system includes an algorithm, shown in Figure 9, to handle the abovementioned cases.  
The overall procedure to stabilize the position of the range gate is as follows. When the circuit system is enabled, memories in FPGA are initialized. Then, the range-gate tracking using an accumulation of Doppler amplitude deviations is performed, and a pulse of _Range gate_ can be generated. If the number of _Range gate_ pulses is 0, it means that there are no blood vessels on a scanline. If the number of _Range gate_ pulses is more than 16, it means that the transducer is not consistently positioned. Therefore, in the flowchart shown in Figure 9, the stage of checking the number of _Range gate_ pulses corresponds to the step of checking whether a transducer is stably positioned.  
In software, a clutter filtering and a spectrum reconstruction are performed with the acquired Doppler raw data. This work does not utilize a typical coherent demodulation to minimally utilize the hardware resources of the FPGA. An echo due to scattering in fluid can be interpreted using the time-shift model of [24]. Then, a Doppler frequency of scatterer in fluid can be estimated with the Doppler raw data without using demodulation. Since the proposed system does not utilize coherent demodulation, the absolute value of the Doppler frequency, that is, the absolute velocity of fluid, can be estimated with a reconstructed spectrogram or spectrum. Thus, if a quantitative evaluation of blood flow is necessary, the system can be applied to blood vessels that can be modeled as low-resistance flow, that is, blood vessels in which the direction of blood flow is rarely reversed.  
In practical measurement conditions, multiple blood vessels may be captured on a scanline, which can generate multiple _Range gate_ pulses. In this case, the system needs to find a representative range-gate position. The system monitors the accumulated ensemble difference, and tracks its peak value. The peak value of the accumulated ensemble difference corresponds to the depth where the fluid velocity is the maximum. We have performed a numerical simulation by considering a single moving scatterer crossing a beam. In the simulation result shown in Figure 10, there is a monotonic increasing tendency between fluid velocity and the peak value of the accumulated ensemble difference. Thus, the system can locate a representative range-gate position on a vessel whose velocity is the maximum by monitoring the accumulated Doppler amplitude deviations.
# Evaluation and Prediction of Higher Education System Based on AHP-TOPSIS and LSTM Neural Network
## 4 Higher Education Prediction Model
### LSTM Neural Network  
Long short-term memory (LSTM) is a special recurrent neural cetwork (RNN). It was introduced by [PERSON] [27] and [PERSON] in 1997. As an excellent variant of RNN, LSTM inherits most of the characteristics of RNN models while addressing the vanishing gradient problem caused by gradual reduction in the gradient back propagation process. Specific to language processing tasks, LSTM is very suitable for dealing with problems that are highly related to time series, such as machine translation, dialogue generation, encoding and decoding. LSTM truly represents or simulates the cognitive processes of human behavior, logical development and neural organization. Since 2014, LSTM has become a very popular research model in RNNs and even deep learning frameworks.  
Figure 4: Framework of the prediction model.  
Figure 3: Education score.  
All recurrent neural networks have the form of a chain of repeating modules of a neural network. In standard RNNs, this repeating module has a very simple structure, such as the repeating units in Figure 5.  
Compared with RNNs, LSTM has a more complex structure, but its repeating modules have a different structure. Unlike a single neural network layer, there are four interacting parts in a very special way. The detailed structure of the current LSTM is shown in Figure 6.  
All the units can make sigmoid nonlinear transformations, and input units can have arbitrary compression nonlinearity. The calculation of the whole LSTM network is described by Formulas (12)-(17):  
\[i_{t}=\sigma\Big{(}W^{i}H+b\Big{)} \tag{12}\]  
\[f_{t}=\sigma\Big{(}W^{f}H+b^{f}\Big{)} \tag{13}\]  
\[o_{t}=\sigma\Big{(}W^{k}H+b^{k}\Big{)} \tag{14}\]  
\[c_{t}=\tanh(W^{c}H+b^{c}) \tag{15}\]  
\[m_{t}=f_{t}\odot m_{t-1}+i_{t}\odot c_{t} \tag{16}\]  
\[h_{t}=\tanh(o_{t}\odot m_{t}) \tag{17}\]  
In the formulas above, \(i_{t}\) represents the input gate, \(f_{t}\) represents the forgetting gate, \(o_{t}\) represents output gate and \(c_{t}\) represents memory cell. \(\sigma\) is the sigmoid function, which is often used as the activation function of neural networks. \(W^{i}\), \(W^{f}\), \(W^{k}\) and \(W^{c}\) are the weight matrices. \(b^{i}\), \(b^{f}\), \(b^{o}\) and \(b^{c}\) are the corresponding offset terms. \(m_{t}\) is the final state of  
Figure 5: The repeating module in standard RNN.  
Figure 6: Structure of the LSTM unit.  
memory cells and \(h_{t}\) is the final output of the memory units. The hidden layer \(H\) consists of the new input vector \(x_{t}\) and the previous hidden vector \(h_{t-1}\) as shown in Formula (18).  
\[H=\left(\begin{array}{c}x_{t}\\ h_{t-1}\end{array}\right) \tag{18}\]
## 4 Conclusions  
XRD demonstrated that a high significant crystalline peak was achieved at 30 nm because it could be reasonably concluded that it had a large grain distribution. As the frequency increased, the \(\chi_{\rm ac}\) decreased sharply. The lowest \(\chi_{\rm ac}\) was detected at 30 nm due to the large grain distribution inducing high coercivity, resulting in an enhanced spin coupling strength. The increased surface roughness could be the induced domain wall pinning effect and also indicated the same tendency. The range of the external field used was not sufficient to rotate the spin state, consequently reducing the spin sensitivity and decreasing the \(\chi_{\rm ac}\) value. The results showed that the maximum \(1.96\times 10^{-2}\) amplitude was obtained at a \(f_{\rm res}\) of 50 Hz for the 20 nm film. The contact angles of the Co\({}_{40}\)Fe\({}_{40}\)V\({}_{10}\)B\({}_{10}\) thin films were less than 90\({}^{\circ}\), which indicates that the films are hydrophilic. The surface energy is closely related to the adhesion of the film. When the thickness increased from 10 to 40 nm, the surface energy changed from 34.2 mJ/mm\({}^{2}\) to 51.5 mJ/mm\({}^{2}\), indicating that the larger the surface energy, the stronger the adhesion. It can be seen that the surface energy tends to increase with an increase in the thickness. The magnetic properties and surface energy efficiency showed that the optimal thickness of the Co\({}_{40}\)Fe\({}_{40}\)V\({}_{10}\)B\({}_{10}\) film was 20 nm, which had the highest \(\chi_{\rm ac}\) value and strong adhesion.  
Conceptualization, S.-L.O. and Y.-T.C.; Methodology, Y.-T.C., W.-H.L., Y.-T.W., T.-H.W., and P.-W.C.; Validation, formal analysis, Y.-H.C. and J.-T.T.; Investigation, Y.-T.C. and W.-J.L.; Resources, C.-L.C.; Writing--original draft preparation, Y.-T.C.; Writing--review and editing, Y.-T.C. and S.-L.O.; Supervision, Y.-T.C. and S.-L.O.; Project administration, Y.-T.C. and Y.-T.W.; Funding acquisition, W.-J.L. and Y.-H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Technology (grant No. MOST108-2221-E-224-015-MY3 and MOST105-2112-M-224-001), and the National Yunlin University of Science and Technology (grant No. 109T0I).  
The authors declare that there are no conflict of interest regarding the publication of this paper. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Case Study
### Wind Power Forecasting Results
#### 4.3.2 Ablation Experiments for the Forecasting Method  
To verify the applicability of the proposed method for seasonal wind power forecasting during cold waves, this paper compares and analyzes wind power forecasting performance of Methods I to IV. The specific details of each forecasting method are shown in Table 7, and the corresponding forecasting results are presented in Figure 15. The RMSE and MAE for the forecasting results are shown in Figure 16. The experimental results indicate that the RMSE and MAE of Methods II to IV and of the proposed method decrease progressively, suggesting that incorporating the cold wave recognition results generated by continuous samples as inputs to the forecasting model can significantly improve the accuracy of wind power forecasting under cold wave events conditions. Compared with those of Method I, the RMSE and MAE of the proposed method are reduced by 2.27% and 1.46%, respectively. Under cold wave conditions with significant wind power fluctuations, the performance of the forecasting model has a substantial impact on the forecasting results. The experimental findings demonstrate that the proposed method outperforms others in wind power forecasting under cold wave events, offering higher forecasting accuracy and applicability.  
Figure 14: Forecasting results for the cold wave season.  
Figure 15: Forecasting results of ablation experiments.
## 2 Materials and Methods
### Data Recordings and Preprocessing  
EEG data were acquired from 62 Ag/AgCl scalp electrodes according to the international 10-20 system using ASA Lab (ANT B.V., Hengelo, The Netherlands). Electrode impedance was kept below 10 k\(\Omega\) throughout the experiment and the sampling frequency was 256 Hz. Additionally, horizontal and vertical electrooculograms (EOG) were acquired.  
Figure 1: (**a**) The experimental design with two tasks and two mental workload levels. The target trials are indicated in red font. (**b**) The analysis flowchart.  
The EEG preprocessing steps involved F-IR band-pass filtering (1-40 Hz), re-referencing to the average of all electrodes and noise removal. The noise from eye movement was eliminated by performing independent component analysis (ICA) [30] and rejecting the components which were highly correlated with the EOG signals. Data were detrended and epochs were extracted based on stimuli and baseline was removed based on (\(-100\) ms, 0 ms) interval before stimulus presentation. Only epochs that corresponded to correct answers by the participants were selected for further analysis in order to alleviate mental workload-irrelevant cognitive stimuli [31]. All preprocessing steps were performed using the EEGLAB toolbox [32] in MATLAB version 2019b (Mathworks Inc., Natick, Massachusetts, USA). An overview of all processing steps is provided in Figure 1b.
## 1 Introduction  
The world is experiencing rapid urbanization. Today, 55% of the global population lives in urban areas [1]. It is estimated that by 2050, the proportion of the people living in cities in the world's total population will increase to 68% (about 6 billion) [1]. Urbanization will not only reduce the area of arable land but will also increase the greenhouse conditions and heat island effects [2, 3, 4, 5]. Hence, in the process of rapid urbanization, the threat of extreme weather changes has become increasingly prominent. Extreme events such as sea level rise, high-intensity rainfall, and storm surge have become major problems that cities must face [6; 7; 8; 9]. New York City (NYC) is the most populous coastal city in the United States. It has extensive beaches, ports, rivers, and wetlands. It is extremely vulnerable to catastrophic flooding because it has 520 miles of coastline [10]. Because of its vast coastal terrain, NYC is extremely vulnerable to various extreme climates such as storm surges, high-intensity rainfall, and sea level rise. With climate change and rising sea levels brought about by urbanization, these risks will increase in the forecast. There are 14 WWTPs in NYC [11], which treat 1.3 billion gallons of wastewater produced by 8.336 million people daily [11]. Meanwhile, the WWTPs in Los Angeles, the second-most populous city in the United States, can treat 400 million gallons of wastewater produced by 4.0 million people every day [12]. That means the WWTPs in NYC can treat 156 gallons per person per day, while those in Los Angeles can just treat 100 gallons per person per day. The WWTPs in NYC have a greater workload. If a WWTP becomes flooded, many people will lose wastewater treatment services. Therefore, it is imperative to accurately predict and evaluate the damage caused by storm surges, high-intensity rainfall, and sea level rise. Previous studies on the impact of floods on WWTPs focused primarily on the WWTP being regarded as a point on the map. However, the WWTP is composed of many facilities. If WWTPs are considered as a point on the map, it is impossible to fully understand the actual flooding situation of the WWTP.  
This study aims to investigate the WWTPs in NYC as the study object, using GIS to analyze the inundation of the WWTP buildings suffered by storm surges, sea level rise, and high-intensity rainfall, and evaluate the impact of various extreme weather situations on the WWTPs. To achieve this goal, we propose a systematic sub-basin creation algorithm based on high-resolution land cover and building footprint data that reflects the microscale flood variation for the WWTPs. In addition, this study also uses the damage assessment formula used by HAZUS-MH [13] to estimate the amount of damage under different flooding conditions. The compounded effect of flooding from sea level rise, storm surge, high intensity rainfall and riverine flooding were analyzed using the concept of total water level.
## 6 Determining the Likely Range of Vertical Impact Angles
### PG-7 Trajectory Characteristics  
The validated PG-7 trajectory model can now be used to estimate the impact angle as a function of horizontal flight distance. The latter is determined by the launch angle of the projectile upon firing. The PG-7 is, however, limited in its maximum flight distance due to the presence of a self-destruction function in the fuse system. This self-destruction function will trigger the warhead after \(\pm 5\) s of flight time. This gives a maximum range of the projectile of approximately 900 m, as already indicated in Figure 25.  
Assuming that the target and the weapon system are at the same altitude, the trajectory as a function of launch angle can be calculated, taking into account the delay of the self-destruction system. Figure 27 shows that a maximum range is reached for a launch angle of \(\pm 9^{\circ}\) (160 mils). For smaller launch angles, the projectile will normally impact the target (or the ground if the target is missed) before the self-destruction function is activated. For larger launch angles, the projectile will detonate in mid-air (this has led to the introduction of a new tactic among certain users of the RPG-7 weapon system, consisting of shooting at a maximum range just above the target in order to have a detonation in the air creating a more lethal and hence effective fragment cloud). As the impact angle increases with increasing launch angle, this also means that the maximum impact angle at the target is reached for a launch angle of \(9^{\circ}\). This is illustrated in Figure 27, where the PG-7 trajectory model was used to determine the impact angle for a set of different firing ranges, including the maximum range. The as such determined impact angles are shown to vary from \(0^{\circ}\) (0 m range) to approximately \(11^{\circ}\) (900 m range), as shown in Figure 28.  
Figure 26: Comparison between the modelled trajectory and the available trajectory data from [4].