esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model-GIS Coupling Strategy
### Mathematical Models
#### 3.2.1 Lattice Boltzmann Method for Velocity Field  
The LBM is commonly labelled as DdQq, where d stands for the space dimension and q is the number of microscopic lattice velocity directions. The possible nodes for 2D lattices are D2Q5, D2Q9. In this study, the D2Q9 model is employed to carry out the airflow velocity field, because it maintains good isotropy of the lattice. The airflow particles distribution on a lattice is shown in Figure 2a.  
The fictitious fluid particles are subject to the lattice Boltzmann equation  
\[f_{i}(\mathbf{x}+ce_{i}\Delta t,t+\Delta t)-f_{i}(\mathbf{x},t)=\Omega_{i}(f),\qquad i =0,1,\ldots,8, \tag{1}\]  
which describes the evolution of distribution function \(f_{i}\) on Cartesian grids with a selective velocity \(\mathbf{e}_{i}\), \(c\) is the movement velocity, \(\Omega_{i}(f)\) denotes the discrete collision operator, and \(\Delta t\) is the time step. The complex collision operator is approximated by using the standard Bhatnagar-Gross-Krook (BGK) scheme [43], which states that the distribution functions is close to a local equilibrium distribution function \(f_{i}^{eq}\) and relaxes toward this equilibrium with a characteristic time \(\tau\). The evolution of the distribution functions using the BGK collision is described by the following equation:  
\[\Omega_{i}(f)=-\frac{1}{\tau}[f_{i}(\mathbf{x},t)-f_{i}^{eq}(\mathbf{x},t)] \tag{2}\]  
Figure 2: Schematic views of particles distribution in 2d lattice.  
where \(\tau\) is the relaxation factor (viscosity of fluid is related to the relaxation time), \(f_{i}^{eq}\) depends on the density \(\rho\) and velocity u of the gas, and is typically defined as  
\[f_{i}^{eq}(\mathbf{x},t)=\omega_{i}\rho(1+\frac{\mathbf{e}_{i}\mathbf{u}}{c_{s}^{2}}+\frac{ (\mathbf{e}_{i}\mathbf{u})^{2}}{2c_{s}^{4}}-\frac{u^{2}}{2c_{s}^{2}}),\qquad\qquad i=0,1, \ldots,8, \tag{3}\]  
where \(\omega_{i}\) is the model-dependent weight coefficient \(c_{s}=\sqrt{RT}\) (\(R\) is the gas constant) is the lattice sound speed. For isothermal flows, \(c_{s}\) is set to be \(c/\sqrt{3}\) with \(c=\Delta x/\Delta t\), where \(\Delta x\) is the lattice spacing (\(c=1\) in this paper). Through the Chapman-Enskog expansion, the macroscopic fluid density \(\rho\), and velocity u, can be derived as the zeroth and first order moments of \(f_{i}\) respectively,  
\[\rho=\sum_{i=0}^{b-1}f_{i},\qquad\rho\mathbf{u}=\sum_{i=0}^{b-1}\mathbf{e}_{i}f_{i} \tag{4}\]  
The fluid pressure is defined directly as \(p=c_{s}^{2}\rho\), the velocity set and the corresponding weight coefficients are defined as  
\[\mathbf{e}_{i}=\left[\begin{array}{cccccccc}0&1&0&-1&0&1&-1&-1&1\\ 0&0&1&0&-1&1&1&-1&-1\end{array}\right] \tag{5}\]  
\[\omega_{i}=\begin{cases}4/9,&i=0\\ 1/9,&i=1,2,3,4\\ 1/36,&i=5,6,7,8\end{cases} \tag{6}\]  
The above model can deduce the Navier-Stokes equation, which describes airflow regularity in the laneway.  
\[\frac{\partial\rho}{\partial t}=-\
abla\cdot(\rho\mathbf{u}) \tag{7}\]  
\[\frac{\partial\rho}{\partial t}+\
abla p+\
abla\cdot(\rho\mathbf{u})=\
abla[\rho v (\
abla\mathbf{u}+(\
abla\mathbf{u})^{T})] \tag{8}\]
# Concurrent Validity of the Inertial Measurement Unit _Vmaxpro_ in Vertical Jump Estimation
## 2 Materials and Methods
### Instruments
#### 2.3.2 Inertial Measurement Unit Vmaxpro  
The IMU _Vmaxpro_(Blaumann and Meyer-Sports Technology UG, Magdeburg, Germany) consists of a triaxial accelerometer, a gyroscope, and a magnetometer (16 g, 4.5 \(\times\) 2.7 \(\times\) 1.2 cm). It has a sampling rate of 1000 Hz [20] and can be attached to metal surfaces with magnets or placed elsewhere with an elastic trap. This IMU can record acceleration data in all three axes and is primarily designed for velocity-based resistance training. From the integration of the accelerations, the device can estimate other related variables such as peak velocity, average velocity, peak eccentric velocity, average eccentric velocity, percentage of force development, percentage of eccentric force development, average propulsive velocity, distance, and duration. Therefore, if the vertical peak velocity data is known, the VJH can be assessed as this corresponds to the take-off velocity [29]. The integration of the acceleration signal is performed internally by the _Vmaxpro_ device and sent instantly via Bluetooth wireless connection (65 Hz) to a smartphone or tablet device with the _Vmaxpro app_ previously installed (Blaumann and Meyer-Sports Technology UG, Magdeburg, Germany). This application allows the user to view the data instantly and export it to a spreadsheet in CSV format. Before each measurement, the device must be calibrated on all its axes by placing it on a completely flat surface on each of the six faces of the octahedron for enough time for the software to recognize them and set the local three-dimensional coordinates of the IMU. Once calibrated, the instrument was placed on an elastic band to be as close as possible to the center of mass, at the subject's hip, according to the manufacturer's specifications [24]. The maximum velocity data obtained were transformed into VJH data according to the equation VJH = \({v_{o}}^{2}/({\it 2g})\)[29], where \({v_{o}}\) is considered as the take-off velocity registered as the maximum velocity recorded by _Vmaxpro_, and \(g\) is the gravitational constant (9.8 m/s\({}^{2}\)).
## 6 Limitations, Final Discussion and Conclusions
### Limitations
#### 6.1.4 Oversimplification of Droplet Dynamics  
Since respiratory droplets carried by exhaled ECA are expected to be overwhelmingly submicron, and thus carried by the fluid flow, the simple dynamical modeling of a starting jet followed by an unstable puff (Section 4) is sufficient to estimate direct exposure distances (this is corroborated in Section 5.3 by comparison with photographs and videos of actual vapers). However, we recognize its limitations: it is strictly valid for a jet/puff system emitted by a motionless vapor in typical indoor conditions with idealized natural ventilation. Evidently, estimating the fluid flows that determine this exposure (and indirect exposure by dispersing droplets) in less idealized conditions requires a more realistic description using computational methods of fluid mechanics to incorporate effects of turbulence and thermal bouyance, as well as air currents from ventilation or motion. Rather, we examined global volume exposure in a separate article through a risk model not involving fluid dynamics described [22]. It is important to mention that this simplification of the dynamics is harder to justify for expiratory activities such as coughing or sneezing, as the latter involve larger ejection velocities and a much wider spectrum of droplet diameters that includes significant number of large supermicron droplets (significant numbers of droplets with diameters 1-10 \(\upmu\)m and even \(>\)100 \(\upmu\)m) whose effect on the dynamics of the carrier fluid cannot be neglected (these are strictly speaking multiphasic flows [52, 60, 87]).
## 1 Introduction
### Simulation with Normal-Hearing Subjects  
Considering the heterogeneity in a group of CI users, it is usually difficult to draw strong conclusions. Additionally, as the FC and CP strategies are fitted to each group of CI users, the heterogeneity of the populations is increased.  
On the contrary, a simulation work, which can be done with NH subjects, allows greater homogeneity of the participants. In this case, the same subject can face different situations [19], such as coding schemes and SNRs, allowing one to focus on different controllable features such as time and amplitude cues and to ensure efficient paired comparisons. However, the results observed with NH listeners cannot be directly extrapolated to CI users and many studies have been conducted on this subject. [PERSON] extensively studied this matter in 2000 [20; 21] and stated that \"performance of the NH listeners established a benchmark for how well implant recipients could perform if electrode arrays were able to reproduce, by means of the cochlea, the stimulation produced by auditory stimulation of the cochlea and if patients possessed neural structures capable of responding to the electrical stimulation\" [22]. They also indicated that the best CI users achieved scores that were within the ranges of scores observed with NH subjects. On the contrary, other authors point out the limitations of using vocoders to simulate electric hearing and the importance of making experiments with CI users [23].  
Consequently, both approaches (with CI and NH subjects) seem necessary; with NH subjects, we can evaluate the consequences of the coding strategies and with CI users we can evaluate the real aspect on a clinical point of view. Practically, for a given strategy, several fitting procedures are recommended by the manufacturers and each CI is fitted to the patient.
## 7 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{3}{c}{**(a)**} \\ \hline \hline \multirow{3}{*}{ \begin{tabular}{c} **Profile** \\ **Spacing** \\ \end{tabular}  } & \multirow{3}{*}{ \begin{tabular}{c} **Depth 1** \\ **(in)** \\ \end{tabular}  } & \multicolumn{2}{c}{**Calculated**} & \multirow{3}{*}{ \begin{tabular}{c} **Spacing 2** \\ **(in)** \\ \end{tabular}  } & \multirow{3}{*}{ \begin{tabular}{c} **Difference 3** \\ **(in)** \\ \end{tabular}  } & \multirow{3}{*}{ \begin{tabular}{c} **Spacing 1** \\ **(in)** \\ \end{tabular}  } & \multirow{3}{*}{\ \begin{tabular}{c} **Calculated** \\ **Spacing 2** \\ **(in)** \\ \end{tabular}  } & \multirow{3}{*}{ \begin{tabular}{c} **Difference 3** \\ **Difference 3** \\ **Difference 3** \\ \end{tabular}  } \\ \hline
4-in & 2.0 & 1.82 & \(-9\%\) & 2.0 & 1.88 & \(-6\%\) \\ \hline
8-in & 2.0 & 1.86 & \(-7\%\) & 2.0 & 1.88 & \(-6\%\) \\ \hline
12-in & 2.0 & 1.86 & \(-7\%\) & 2.0 & 1.88 & \(-6\%\) \\ \hline
16-in & 2.0 & 1.83 & \(-9\%\) & 2 The calculated values were consistent with the beam's drawing, with depth and spacing variations of less than 10% for the two sets of prestressed tendons on each side of the box beam. However, the center prestressed tendon showed significant discrepancies, with differences of up to 23% compared to the drawing values. These variations in the center prestressed tendon are significant. Given the consistency of the calculated rebar depths and the GPR device calibration specifically for this concrete box beam, the difference between the calculated values and the depths shown in Figure 2 is attributed to construction flaws. A summary of the maximum and minimum values calculated for different GPR profile offsets is provided in Table 7.
# Collaborative Use of a Shared System Interface:
## 4 Methodology
### Material and Apparatus  
We used SmartEye Pro (SmartEye, Gothenburg, Sweden) eye-tracking system, which enables a non-invasive temporally synchronous collection of gaze and pupil data of multiple participants. This system employs two cameras per participant for large field of view and permits measurement of a head orientation and gaze direction in 3D, including the ability to discriminate between gaze directed at the interface or at the other user in the dyad. SmartEye Pro also permits measurement of pupil diameter in real-time during the task, and it provides a great gaze accuracy (around 0.5 degrees [42] for a 30 cm eye distance--a 1 degree gaze accuracy is considered high in the eye-tracking literature [43; 44; 25; 45]). It was configured with a sampling rate of 60 Hz. To synchronize computer clocks at all times during the studies, we configured a Network Time Protocol (NTP) Server. NTP protocol is designed to synchronize several computers' clocks across variable-delay networks, with an accuracy below one millisecond between network devices [7].  
The experimental stimuli were developed and administered using E-Prime 3.0 software (Psychology Software Tools, Sharpsburg, MD, USA). The software ran on a computer with a clock perfectly synchronized with SmartEye computers through the NTP server. A crucial benefit of E-Prime is that it is able to provide a rich set of time stamps for every event or display, allowing for direct matching with our resulting eye movement data. Moreover, E-prime permitted synchronized acquisition of questionnaire data during the experimental tasks. The stimuli were run on two identical computer monitors connected to the same computer central processing unit (CPU) to permit shared display.  
In addition to the two SmartEye cameras, a video camera was fixed on the top of each user's computer monitor. The audio and video of each user's face, which were sampled by these cameras during the two experimental tasks were recorded with Media Recorder software (Noldus, Wageningen, The Netherlands), thereby permitting future assessment of behaviors such as head orientation and characteristics of auditory communication. Observer XT software (Noldus, Wageningen, The Netherlands) synchronized Media Recorder recordings to our eye-tracking recording through time stamps that were accurately linked to the absolute time of the study as provided by the NTP server.  
Figure 3 depicts the physical setup used for both studies.
## 3 Deepfake Technologies and the Emergence of Deepfake-Driven Social Engineering
### The Definition of Deepfake-Driven Social Engineering  
_Deepfake-driven social engineering_ represents an emerging threat paradigm where malicious actors use AI-generated or manipulated media to deceive targets, often bypassing traditional security controls [1]. In classic social engineering, attackers exploit human psychology, using impersonation, authority, or urgency to prompt victims into revealing confidential information or performing unauthorized actions. Deepfake-driven social engineering intensifies these tactics by adding hyper-realistic audio, video, or images of trusted individuals.  
For instance, a deepfake video might depict a Chief Executive Officer (CEO) urgently requesting a wire transfer. Alternatively, a synthetic voice message could appear to come from a trusted stakeholder demanding sensitive documents. These AI-driven impersonations can be remarkably believable, increasing the risk that employees or even advanced security systems will fail to detect the deception [8]. The psychological cues that make social engineering successful--such as perceived authority or familiarity--become more convincing when backed by near-flawless digital impersonations [8].
# Hierarchical Gated Recurrent Unit with Semantic Attention for Event Prediction
## 5 Conclusions  
Event prediction has important value in government decision-making and financial investment. Previous methods are mainly based on the time series of events for prediction, without considering the impact of prior knowledge on event prediction. In this paper, we construct a novel model, called Hierarchical Semantic Gated Recurrent Unit (HS-GRU), which is a hierarchical event prediction model based on time series and prior knowledge for the task of events prediction. HS-GRU adopts the semantic selective attention mechanism to fuse time-based event information and prior knowledge for event prediction. It does not need hand-crafted event features, which can be applied in many domains. Our model consists of three layers. The encoding layer encodes the time-based event information and the prior knowledge of the current event link by Gated Recurrent Unit (GRU) and Association Link Network (ALN), respectively. The attention layer adopts the semantic selective attention mechanism to fuse time-based event information and prior knowledge and calculates the current event link embedding, instead of relying on time series. The prediction layer takes the event link embedding as an input and generates the next event with GRU decoder. Experiments demonstrated that the accuracy of our model is significantly better than the state-of-the-art methods. Because the model does not have domain features, it has important value in multiple applications such as emergency warning,  
Figure 5: Event selection example. It verifies whether the model can find the correct event in the candidate set by the prediction result.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**International News**} & \multicolumn{4}{c}{**China News**} \\ \hline  & **Top 1** & **Top 5** & **Top 10** & **Top 1** & **Top 5** & **Top 10** \\ \hline Random & 2.00\% & 10.00\% & 20.00\% & 2.00\% & 10.00\% & 20.00\% \\ \hline GRU & 56.00\% & 74.36\% & 78.00\% & 49.27\% & 69.27\% & 71.64\% \\ \hline HRED & 59.64\% & 74.73\% & 79.64\% & 53.82\% & 71.45\% & 73.45\% \\ \hline
**HS-GRU** & 69.45\% & 82.73\% & 86.36\% & 56.36\% & 75.82\% & 78.91\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The result of event selection task on two datasets.  
traffic forecast, stock prediction, and disease spreading. In the future, we will consider abandoning the traditional Recurrent Neural Network (RNN) model for processing sequence structures and discuss the method for predicting events using semantic networks which encoded by the Graph Neural Network.  
methodology, Z.S.; validation, Z.S.; writing--original draft preparation, Z.S; resources, J.J.; writing--review and editing, Z.S. and J.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# A 12 dBm B1 dB N-Path Notch Filter for Transmitter Leakage Suppression in Wideband Receiver
## 4 Circuit Implementation
### N-Path Filter Architecture of Stage I and Stage II  
A detailed schematic is presented in Figure 5. Within the BPF, off-chip quarter-wavelength transmission line transformation is employed, with the resultant impedance affecting both the blocking power and the depth of band rejection. All filter capacitance is realized using MIM capacitors. The N-path BPF and BRF switches utilize NMOS transistors with a low threshold voltage and a 2.5 V drain and source breakdown voltage to achieve high linearity. The rejection depth is enhanced by minimizing parasitic resistance in the switch and capacitor RF lines [18]. After conversion, the impedance in the blocking band is reduced to improve blocking linearity. Parasitic resistance in the switches also impacts the filter's insertion loss. The N-path filter's switch size W/L is equal to 240 \(\upmu\)m/220 nm and achieves an on-resistance of approximately 4 \(\Omega\), which reduces passband insertion loss and  
Figure 9: Inverted impedance curve of proposed notch filter at \(f_{LO}\) = 450 MHz.  
enhances notch rejection. The NMOS drain and source are biased with a 0.5 V voltage to optimize linearity.
## 4 Case Study  
### Location Identification  
In this section, the proposed approach is applied to evaluate and select locations for WWS in Vietnam. After excluding military and conservation areas, this study considered twenty feasible locations along the coast, numbered in ascending order from north to south as illustrated in Figure 4. In addition, the distance to the shore of these locations was greater than 25 km to avoid negative impacts on other economic and social activities [14]. The locations' geographical coordinates are presented in Table A1.
## 3 Results and Discussion
### Industrial Virtual Water Consumption in Gansu Province between 2007 and 2012
#### 3.1.3 Benefit Analysis of Virtual Water in Gansu Province  
The results of the current study show a large gap in virtual water use between the three industry categories in Gansu between 2007 and 2012, but the total combined water use did not change significantly during this period. These findings indicate that, although Gansu province has made advances in various industries, inequitable distribution of water resources among the industries is still an issue. Upon examination of both gross production value and virtual water distribution, it appears that water consumption remains stable for each individual industry class, but that gross production value increases, suggesting an improvement in utilization efficiency. The primary industry class consumed the highest proportion of virtual water but exhibited the lowest production value, while the secondary and tertiary industry classes showed the opposite trend (Figure 3).  
The added value of primary industries in Gansu province between 2007 and 2012 (17.063, 19.412 and 20.079 billion yuan, respectively) was lower than the added value of the national primary industry class (27.075, 37.895 and 49.391 billion yuan, respectively) [62; 63; 64; 65; 66; 67]. This trend is related to grain output and prices; Gansu province's grain output and grain prices were lower than the national level, further indicating that the virtual water distribution in Gansu province in the primary industry is not appropriate. It is suggested that, to remedy this result, Gansu should adjust the distribution of water according to specific conditions (e.g., spatial distribution of water resources within Gansu  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**Total Virtual Water in 2007**} & \multicolumn{3}{c}{**Total Virtual Water in 2010**} & \multicolumn{3}{c}{**Total Virtual Water in 2012**} \\ \cline{2-7}  & **Local Production** & **Field Input** & **Local Production** & **Field Input** & **Local Production** & **Field Input** \\ \hline Primary Industry & 210.92 & 1.11 & 175.84 & 1.11 & 226.64 & 1.61 \\ Secondary Industry & 56.13 & 12.94 & 65.05 & 19.68 & 80.40 & 57.40 \\ Tertiary Industry & 8.16 & 2.07 & 11.70 & 3.73 & 18.89 & 27.65 \\ Total & 291.33 & & & 277.11 & & 412.59 \\ \hline \end{tabular}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{Unit: 100 million m\({}^{3}\).} \\ \end{tabular}
\end{table}
Table 2: Total virtual water consumption of primary, secondary, and tertiary industries in Gansu province from 2007 to 2012.  
Figure 2: Virtual water flow momentum of primary, secondary, and tertiary industries in Gansu province from 2007 to 2012.  
province) through trade within the province, encourage the development of water-saving and profitable industries, and reform those industries that are water-consuming and less profitable [68; 69].