esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of Ultra-High-Dose Rate Electron Beams with ElectronFlash [PERSON]
## 2 Materials and Methods  
### Description of the Electron Linear Accelerator System  
The FLASH electron linac installed at Institut Curie (Orsay) is based on a standing wave (SW) accelerating structure. It is 52 cm long and it works at 2.998 GHz (S-band) in the \(\pi\)/2 resonant mode, with a bi-periodic geometry in which the accelerating cells are alternated with unexcited coupling cells. The nominal energies of the electron beam are 5 MeV or 7 MeV, for both FLASH and conventional radiation operations. A thermionic gun generates electrons with pulse peak currents up to 120 mA, which are necessary for the FLASH mode. The linac is powered by an S-band magnetron delivering 3.1 MW [19] with the RF pulse adjustable between 0.5 and 4 us. The magnetron is protected by a circulator from accidental power reflections in the waveguide of the SW Linac. The linac parameters are summarized in Table 1.  
In conventional mode, two ionization chambers are used for online dosimetry. They are automatically displaced from the beam axis when the FLASH regime is chosen because of their saturation when a high current per pulse is delivered. In this case, the beam current can be measured through a pair of toroids which also provide the pulse time duration.  
An external Faraday cup monitoring system consisting of an aluminum cylinder 5 cm thick and 12 cm large was used. Almost all charges can be collected in the Faraday cup (>93%) according to the Albedo factor [20]. The Faraday cup is connected through a high-quality coaxial wire to an oscilloscope which allows the measurement of the total charge per pulse and the time pulse profile. The system has a nano-second time resolution with the advantage of not saturating, even with a high charge in the electron pulse [21; 22; 23; 24].  
For the measurements of the dose parameters and profiles, two poly-methylmethacrylate (PMMA) cylindrical applicators were used. The PMMA applicators have a 5 mm wall thickness and 30 mm (A-d30) and 120 mm (A-d120) inner diameters, with a source-surface-distance (SSD) of 55 and 110 cm, respectively. Because of the charge interaction with PMMA walls, the current decreases by approximately 50% at the end of the applicators. The accelerator head was able to irradiate both in the vertical and horizontal directions. The vertical one is usually dedicated to in vitro irradiation while the horizontal one is for in vivo irradiation.
# Analog Circuit Fault Diagnosis Using a Novel Variant of a Convolutional Neural Network
## 5 Conclusions  
Aiming at the problem that the 1 DCNN network performs poorly in the fault diagnosis of complex analog circuits, this paper proposes the MSCNN-SK network. On the basis of the 1 DCNN network, this network adds a multi-scale average deviation layer to calculate the 0 th-order, 1 st-order and 2 nd-order average deviation sequences, extracts the average rate of change characteristics of the circuit's time domain response signal, and introduces the dynamic convolution kernel selection mechanism that enables the network to adaptively  
Figure 8: Two-dimensional scatter distribution of original data and features extracted from each network model in Four-opamp biquad high-pass filter circuit formed by T-SNE method. (**a**) Circuit fault data. (**b**) Features extracted by the BPNN network. (**c**) Features extracted by the RNN network. (**d**) Features extracted by the LSTM network. (**e**) Features extracted by the 1 DCNN network. (**f**) Features extracted by the MSCNN-SK network.  
select a suitable size convolution kernel during the feature extraction process without manual intervention.  
Through the simulation experiments of two benchmark test circuits, the fault diagnosis comparison experiment was first carried out. Networks such as BPNN, RNN, LSTM and 1 DCNN were selected as the comparison methods. The results show that the MSCNN-SK network can identify the fault of the Shallen-Key band-pass filter circuit 100%. In the more complex Four-opamp double second-order high-pass filter circuit, the average fault diagnosis accuracy rate has reached 99.87%. At the same time, after using t-SNE technology to visualize the features extracted by each model in two dimensions, the features extracted by the MSCNN-SK network have the highest separation. In the two-dimensional visual comparison of the features extracted by each model, the features extracted by the MSCNN-SK network are also the most discriminative.  
The proposed MSCNN-SK network can be directly used for other fault classification cases. In future work, we will apply the proposed MSCNN-SK network to more analog circuit fault diagnosis to further evaluate its generalization ability.  
Writing--original draft preparation, L.H.; writing--review and editing, F.L.; software, K.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Preliminary research on the on-line monitoring system and key technologies of high-speed railway ballastless track line diseases based on distributed optical fiber vibration sensing (DAS)] grant number [Beijing-Shanghai Research-2019-166].  
The data used in this article are all generated by PSPICE system simulation.  
The authors declare no conflict of interest.
## 2 Model Definition
### The Environment \(E\)  
The spatial component of the model is considered a three-dimensional continuous space \(E\subset\mathbb{R}^{3}\) with length, width, and height of \(L,W,\) and \(H\), respectively. The space \(E\) is discretized in all three dimensions with size \(\delta\) represented by a matrix \(M_{E}\in\mathbb{R}^{L/\delta\times W/\delta\times H/\delta}\). Each cell \((i,j,k)\) of the space matrix \(M_{E}\) represents a cube with a center of mass at \(((i+0.5)\cdot\delta,(j+0.5)\cdot\delta,(k+0.5)\cdot\delta)\) and with a volume \(\delta^{3}\).  
The space \(E\) contains a set of objects and individuals. Formally, each location in the space \((i,j,k)\in E\) is either filled by an object (which can be an individual) or by air. If a location is filled by an object, air is not presented and vice versa. We assume that all objects in the space are static and therefore do not move over time. A location with air has four properties: pathogen particles \(\lambda\in\mathbb{N}\), velocity \(v\in\mathbb{R}^{3}\), acceleration \(a\in\mathbb{R}^{3}\), and pressure \(\rho\in\mathbb{R}^{3}\). Furthermore, we assume that the air in the space is incompressible.  
The air in the space is moving based on the incompressible Navier-Stokes equations [61]:  
\[\frac{\partial\theta}{\partial t}+u\cdot\
abla\theta=\frac{-\
abla\rho}{\rho }+\mu\
abla^{2}u+g, \tag{1}\]  
where \(\theta=(u,v,w)^{T}\) is the velocity vector, \(t\) is time, \(\rho\) is the uniform density of the atmosphere, \(\mu\) is the dynamic viscosity, and \(g\) is the gravity constant vector. The pathogen particles are airborne and move with the air as a result. In addition, as pathogen particles cannot survive outside the host for too long, the number of pathogen particles generated by an infected individual in a single breath decays exponentially over time [62; 63]. As such, for each \((i,j,k)\in E\), the number of pathogen particles satisfies:  
\[\frac{\partial C}{\partial t}=C\big{(}\
abla\tau-\
abla u-D\big{)}, \tag{2}\]  
where \(C(x,t)\) is the number of pathogen particles in location \(x\) at time \(t\), \(\tau\) is the tensor of turbulent diffusivity, and \(D\) is the decay rate of the pathogen particle.  
For the initial conditions, it is assumed that the entire air is without pathogen particles and \(\forall x:u(x,0)=0\) (i.e., the \"static\" state). The boundary conditions are defined to be the boundary cells of the space matrix \(M_{E}\) and all locations that contain an object. Namely, open boundary conditions (stress-free boundary conditions) are specified at the outlet. No-slip boundary conditions are provided in all four boundary directions [62].
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 2 Materials and Methods
### Cross Entropy Loss Function  
The cross entropy loss function is a loss function commonly used in dealing with classification problems, expressed as the difference between the observed probability distribution and the predicted probability distribution [34]. The function formula is shown in Equation (6).  
\[Loss=-\sum_{i=1}^{c}p(x_{i})\log(q(x_{i})) \tag{6}\]  
where \(p(x)\) is the observed distribution of the sample, \(q(x)\) is the predicted distribution, and \(c\) is defined as above. The smaller the value of _Loss_, the closer the predicted output is to the true sample label and the better the model predicts.
# Dimensional Control of Aircraft Transmission Bodies Using CNC Machines and Neuro-Fuzzy Systems
## 3 Research Methodology
### Case Study--Bearing Seat  
One of the holes of the bearing seat, shown in Figure 7, was selected to present measurement data, research methodology and the results of work. To determine the curvature of the base face, measurements were taken at 65 points spaced evenly on the base surface (Figure 7a). To determine the position of the axis and the diameter of the hole, the coordinates of four measurement points were used, arranged in one plane every 90 degrees on the inner cylindrical surface of the bearing seat (Figure 7b). The hole axis position error was determined as the difference between the nominal position (point B) and the position resulting from the measurement (point A). Hole position deviation is the distance between points A and B. The methodology described relates to measurement on CNC and CMM.  
As variables for learning neuro-fuzzy models for the presented bearing seat, the following geometric parameters were measured on a CNC machine tool and in a measurement laboratory using CMM:  
1. Hole (bearing seat) position deviation (HPDCMM)--measurement on the CMM in a free state;
2. Hole (bearing seat) position deviation (HPDCNC)--measurement on the CNC machine in the mounted state;
3. Curvature of the reference surface of the cover (SCCMM)--measurement only on CMM in a free state.  
The following results were obtained as shown in Table 1 and Figures 8 and 9. The tolerance of the hole position is 0.25 mm.  
Figure 7: Cover after machining with distribution of measuring points. (**a**) Base face of the cover with marked measuring points used to determine the curvature of the surface. (**b**) Bearing seat with four measuring points used to determine, among others, the center of the hole (point A). Point B is the nominal position of the hole. Hole position deviation is the distance between points A and B.  
This selection of features determining the position of the bearing seat was chosen as a result of preliminary tests. Their results are summarized in Section 4.1, where the data analysis is described.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Body**} & **Hole Position Deviation** & **Hole Position Deviation** & **Curvature of the Cover Reference** \\  & **Measured on CMM** & **Measured on CNC Machine** & **Surface Measured on CMM** \\
**Number** & **HPD\({}_{\text{CMM}}\)/mm** & **HPD\({}_{\text{CNC}}\)/mm** & **SC\({}_{\text{CMM}}\)/mm** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: Data from measurement systems.  
Figure 8: Deviations of the hole position.  
Figure 9: Curvature of the reference surface of the cover.
## 2 Materials and Methods
### Adsorption Experiments  
The adsorption behavior of the anionic dyes (MO and FCF) on the surface of Fe\({}_{3}\)O\({}_{4}\)@MSN-PDMAEMA and Fe\({}_{3}\)O\({}_{4}\)@MSN-QPDMAEMA was explored as a function of changing the initial concentrations, exposure time, and the solution's pHs. Generally, 10 mg of the sorbent was suspended in 15 mL of a solution containing a specific amount of the selected dye at 25 \({}^{\circ}\)C and a shaking rate of 150 rpm. The sorbent material was separated by centrifuge at a specific time. The amount of the chosen dye adsorbed on the surface was estimated using UV/Vis spectrophotometer. The adsorption capacity (q\({}_{\rm e}\)) at equilibrium (mg\(\cdot\)g\({}^{-1}\)) was calculated using Equation (1).  
\[\rm q_{e}=\frac{(C_{o}-C_{e})V}{m} \tag{1}\]  
where C\({}_{0}\) is the dyes' initial concentration (mg\(\cdot\)L\({}^{-1}\)), and C\({}_{\rm e}\) is the concentration of dyes at equilibrium (mg\(\cdot\)L\({}^{-1}\)). V and m are the volume (L) and the mass of adsorbents (g), respectively.
## 2 Methods
### Design of Suitable System Configurations  
The objective of this task is to systematically design suitable system configurations based on the chosen topology. All theoretically possible integration points for heat exchangers for heat extraction or supply into the plant were systematically investigated through system simulations using the Epsilon Professional(r) software, version 16, developed by Iqony Solutions GmbH, based in Essen, Germany. The created universal system simulation tool is depicted in Figure 3 and represents the flow diagram from Figure 1a, supplemented with optionally switchable heaters and coolers between the mandatory main components. The following variations were analyzed:  
* Closed and open cycles. * Compressor outlet temperature CDT of the charging line (manipulated variable): 450 \({}^{\circ}\)C (state of the art for turbo compressors) or 625 \({}^{\circ}\)C (a realistically achievable and economically justifiable development stage). The compressor outlet pressure serves as the control variable here. The Epsilon Professional component 39 (controller with internal target value) is used here. * Working fluids: air, as well as CO\({}_{2}\) and argon for the closed concepts. * For the air-operated system, variants with a recuperator for transferring the heat from the hot to the cold side of the charging line (and optionally the discharging line) were also considered. Additionally, the idea of providing other technologies for expansion and compression such as screw or piston machines was considered. However, a literature review revealed that axial turbomachinery is indispensable for the desired operating range (high power at moderate pressures) [37]. The investigation of concepts based on reversible units, which avoid separate cycles for charging and discharging, was also considered. However, such turbomachinery is not state of the art. A literature review on the modeling of reversibly operating turbomachinery has only yielded the work of [PERSON] [33]. The model equations contained therein require detailed knowledge of the construction of the turbomachinery used, making it impossible to create a generic model suitable for system simulations. Since turbomachinery can only be optimally designed for one flow direction, the use of reversible machines also carries  
Figure 3: Simulation tool in Epsilon Professional. the disadvantage of significantly lower efficiency, which likely outweighs the theoretical advantage of potentially lower investment costs in the case of Brayton batteries, as Brayton batteries already have limited efficiency potential in a realistic, non-idealized consideration. Therefore, the focus was limited to systems based on turbomachinery, and separate circuits for charging and discharging are provided. The resulting concept matrix, which was worked through by multiple system simulations, is presented in Table 3.  
The coolers C and heaters H, which are arranged between the main components in Figure 3 and can optionally be activated by on/off switches in value inputs (Component 46
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 4 The Proposed Framework for Travel Time Prediction
### Spatiotemporal Data Refinement and Categorization
#### 4.1.3 Travel Time Filtering  
Accurate travel time data are fundamental to the development of reliable transportation models and predictive systems. To identify the most suitable distribution for modeling our dataset, we performed a comprehensive analysis of the raw travel time data. The Probability Density Function (PDF) of the travel time data is shown in Figure 5, with the \(x\)-axis representing travel time in seconds and the \(y\)-axis indicating the frequency of observations.  
As illustrated in Figure 5, the travel time data follow a truncated normal distribution, with a lower bound of 1 s and an upper bound of 11,995 s. These extreme values are atypical for taxi trips within NYC and are classified as outliers. The distribution exhibits  
Figure 4: Distribution of a GPS entry sample.  
Figure 5: Distribution of the travel time data.  
a left-skewed pattern, with a mean travel time of 785 s. The presence of a long right tail further indicates the existence of outliers, which, if not addressed, could significantly skew the analysis and interpretation of the data. The proper handling of these outliers is essential in ensuring the robustness and accuracy of subsequent modeling efforts.  
To eliminate erroneous trip durations, we conducted a geospatial analysis using ArcGIS Pro. The XY to Line tool was employed to visualize all travel routes, enabling the evaluation of travel time concerning the distance traveled for each trip. Trips with abnormally long or short travel times compared to the distance traveled were subsequently removed from the dataset.
## 4 Discussion
In order to produce more ATPs, the aerobic respiration in fungal hypha was enhanced while the glycolysis pathway was checked to a great extent. Such reduction in glycolysis was supported by the decreased level of lactate in D group. In addition, a marked decrease in phenylalanine, alanine, aspartate, and arginine levels was observed in D group. The glycogenic amino acids underwent degradation [29] and reflected an enhancement in the gluconeogenesis pathway that compensates the deficiency of glucose. The results further suggested that carvacrol helps produce energy and amino acid metabolic disorders in _P. digitatum_. A significant increase in the transcriptional initiators e.g., uracil and adenine were found in the D group compared with the CK group (Table 2) [30]. The increased level of uracil and adenine revealed that carvacrol caused RNA damage to _P. digitatum_. In this pioneering study, the antifungal activity of carvacrol against _P. digitatum_ was studied by using the metabolomics approach based on \({}^{1}\)H-NMR. Carvacrol can cause an abnormal metabolic state of _P. digitatum_ by interfering with different metabolic pathways such as energy and amino acid metabolisms. Furthermore, carvacrol could also induce damage to the RNA molecules and ROS production. Metabolomics provide a powerful and feasible tool for evaluating antifungal activity and exploring its potential mechanism. Figure 6: **(A)** In this study, metabolic analysis was used to analyze the pathway topology related to antifungal activity. The term “log p” is the conversion of the original p value calculated from enrichment analysis, and “impact” is the path impact value calculated from the path topology analysis. The bubble area is proportional to the effect of each path, and the color indicates the importance from the highest red to the lowest white. **(B)** Alanine, aspartate, glutamate metabolism; **(C)** glutathione metabolism. **Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/9/11/2240/s1](http://www.mdpi.com/2076-3417/9/11/2240/s1)]([http://www.mdpi.com/2076-3417/9/11/2240/s1](http://www.mdpi.com/2076-3417/9/11/2240/s1)). **Author Contributions:** Conceptualization, C.W. and J.C.; methodology, C.W. and C.C. ; formal analysis, C.W. and C.C. ; investigation, Y.S. ; W.Q. and C.C. ; data curation, C.C. ; writing--original draft preparation, C.W. ; Y.S. ; W.Q. and C.C. ; writing--review and editing, C.W. and M.F.N. ; supervision, C.W. ; project administration, C.W. and C.C. ; funding acquisition, J.C.  
**Funding:** This research was funded by National Natural Science Foundation of China (NO.31760598) and Advantage Innovation Team Project of Jiangxi Province (NO.20181 BCB24005). **Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
Research demonstrates that the situational awareness level of remote tower controllers directly influences the safety and efficiency of the air traffic control operations [12]. Mitigating and minimizing situation awareness-related mistakes in remote tower operations, together with enhancing human dependability, are essential for guaranteeing the safe functioning of remote towers. Researchers from the German Aerospace Center (DLR) used questionnaire surveys [13] and on-site validations [14] to look at how situational awareness, operational methods, and system usability changed when the equipment and workstations were changed. Utilizing these methodologies, they investigated the alterations in situational awareness and perceptual capacities of distant tower controllers under various traffic conditions. Controllers must sustain situational awareness throughout operations to identify dynamic targets, including aircraft, ground vehicles, and dangers like birds. The most efficient approach to enhance air traffic control capabilities and safety is through efficient performance monitoring [15]. Effective situational awareness in controllers can guarantee the secure and effective functioning of air traffic control. Nonetheless, a reduction or loss of a controller's situational awareness may result in perilous circumstances, such as aircraft delays or air traffic congestion, with potentially catastrophic repercussions. Consequently, it is essential to accurately identify and evaluate the situational awareness levels of remote tower controllers.
# LDPC-Coded CAP with Spatial Diversity for UVLC Systems over Generalized-Gamma Fading Channel
## 5 Conclusions  
In this paper, we studied the performance of LDPC-coded CAP with spatial diversity for UVLC. Expressions for approximated BERs were derived for systems without FEC code. Furthermore, Monte Carlo simulation was used to verify the analytical study of the uncoded system. We showed that the diversity scheme was effective in mitigating the effect of turbulence, and further studied the performance of the LDPC-coded scheme with and without diversity over turbulent channels. Obtained results showed that the LDPC-coded scheme without diversity was not enough to combat strong turbulence, while spatial diversity with LDPC codes offered the best performance. In turbulence with \(\sigma_{I}^{2}=1.4\), the BER of the CAP UVLC system without coding and diversity was close to \(10^{-1}\) at the SNR of 30 dB, while the LDPC-coded CAP with a spatial-diversity system with \(N=4\) achieved a BER of \(10^{-4}\) around SNRs of 19.5 and 27.5 dB for 4- and 16-CAP, respectively. Results demonstrated that the effect of turbulence on BER performance could be effectively mitigated by spatial diversity and LDPC codes.  
Conceptualisation, H.J. and S.R.; methodology, H.J. and S.R.; software, H.J. and S.R.; validation, H.J. and S.R.; investigation, H.J., S.R., and N.H.; writing--original-draft preparation H.J. and S.R.; writing--review and editing, S.R., W.P., and Z.A.; supervision, S.R. and H.Q.; funding acquisition, N.H. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China (grant numbers 61961008,61661016, and 61761014), and in part by the director fund project of Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing (grant number CXKL06180101).  
The authors declare no conflict of interest.