esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform
## 3 Related Works  
Recently, previous works have shown an increased interest in ad fraud detection and focused on various solutions for detecting ad fraud. Several approaches are presented for different advertising fraud scenarios, including botnet [3; 5], click and conversion [1; 2; 7; 8; 9; 10], and RTB [3; 10; 11; 12; 13; 14; 15]. In case of handling bot traffic, [PERSON] analyzed the traffic generated by the malware to study the behavior of a bot sample and to identify the location of the bot's server [6], and [PERSON] scored ad traffic to classify the intentional and non-intentional ad traffic in real time [3]. For the click and conversation fraud, [PERSON] built a realistic-looking landing page to observe and measure click-spam across ten major ad networks and four types of ads [2], and [PERSON] developed conditional random fields (CRF) to learn users' click behaviors [8]. In case of PPV and PPC networks, [PERSON] identified pay-per-view (PPV) networks to analyze the purchase traffic coming from a set of honey websites and inflate advertising impressions on websites [9].  
In addition, there have been several studies using machine learning techniques to build a model to classify and predict fraudulent activities as well as user behaviors in online advertising [8; 16; 17; 18]. Specifically, [PERSON] and [PERSON] proposed the prediction of user visits and viewability for online display advertising by building models based on a probabilistic latent model [19; 20]. In addition, [PERSON] used the clustering method to investigate a novel crowd fraud detection method for search engine advertising [5]. Similarly, [PERSON] presents a machine learning framework based on logistic regression for modeling response prediction in display advertising [16]. However, these supervised and semi-supervised learning approaches have not seemed to be efficient in detecting fraud by using the real DSP bid request dataset in RTB.  
From a methodological point of view, [PERSON] has designed a system for the detection of invalid ad traffic [3]. This paper classifies invalid traffic by computing the Shannon Entropy of the distribution of bid requests across IP addresses for each domain. On the other hand, [PERSON] analyzed a dataset containing transactions for ingress and egress ad traffic from an ad network to detect fraudulent activities based on publishers by determining dynamic thresholds in some features to detect anomalous ad traffic [6]. The research to date has tended to focus on detecting and classifying non-intentional traffic in the context of publishers rather than audiences. For this reason, they may ignore non-intentional bid requests that do not come from a group of abnormal publishers. In this research, we provide another aspect of detecting fraud traffic based on the perspective of the audience. This provides an ability not only able to detect non-intentional bid requests from groups but also the individual. Moreover, we also offer a system that has the feasibility to be integrated into the RTB process to filter non-intentional bid requests.
# Chaotic Characteristic Analysis of Spillway Radial Gate Vibration under Discharge Excitation
## 3 SRG Analysis of the Prototype Test Data
### Project Introduction
There is a gap in the connecting part of the fixed and movable hinges, which may increase the vibration intensity. In addition, it can be seen that the vibration of each measuring point on the supporting arm obeys no obvious rule with the change in the opening heights, and it is difficult to draw an intuitive conclusion. Hydrodynamic loads were the key factors that caused the structural vibrations of the radial gate. When the flow velocity was higher than 5 m/s, the fluctuation and violent turbulence occurred based on the jet flow. Therefore, it was important to determine the velocity field distribution in order to analyze the vibration characteristics of the spillway radial gate, as no comprehensive and continuous flow field information could be obtained due to the limited nature of the prototype test. Numerical simulations that adopted the RNG \(k\)-\(\epsilon\) turbulent model were performed using Ansys Fluent 2021R1 software to simulate the flow field [20,21], which served as inputs for the subsequent analysis of the gate vibration in this paper. Its grid diagram is shown in Figure 4, with a total grid of 2.94 million; Figure 5  
Figure 2: Diagram of the SRG: (**a**) layout of SRG (No. 3#) acceleration measuring points; (**b**) partial opening diagram of the SRG. shows the schematic diagram of the open flow pattern. According to the flow field derived from the numerical simulations, the flow pattern after the water flowed out of the bottom edge of the gate was relatively smooth, which presented a typical free outflow pattern. The flow velocity distribution of the bottom edge under different gate opening heights is shown in Figure 6. Note that the 2D profile presented in Figure 6 is located on the x-z plane on the center line of the spillway, as shown in Figure 5. It can be seen that the high-flow-velocity zone (flow velocity higher than 5 m/s) under working condition 1 presents the greatest range of effect, followed by working condition 2 and finally working condition 3 that has the least range of effect. When comparing the maximum flow velocity of the bottom edge, we derived the formula of condition 3 \(>\) condition 1 \(>\) condition 2, where no qualitative rule could be determined. Hence, it was necessary to analyze the chaotic characteristics of the gate's nonlinear vibration response. Figure 4: Three-dimensional grid diagram. Figure 3: RMS of the vibration acceleration of measuring points on the supporting arm.
## 4 Numerical Results and Discussion
From Figure 6, the OP decreases when \(\eta\) increases from 0 to 1, and the OP in the maximum capacity case is better than the non-maximum capacity case. Here, the simulation results agreed with the mathematical analysis of the above section. Furthermore, the OP and the EC of the proposed system in connection with the number of users are presented in Figure 7a,b. Similarly, the OP decreases and the EC increases, while the number of users varies from 0 to 10 and have better values in the maximum capacity case. In all of them, the simulation and analytical mathematical results agreed well with each other. Figure 5: Comparison of the outage probability (**a**) and ergodic capacity (**b**) versus \(\mathrm{P_{s}/N_{0}}\). Figure 6: The outage probability versus \(\eta\).
## 3 Methodology
Finally, it is essential to continuously monitor the operational process to ascertain whether the inefficiencies have been resolved and the production process is optimized. The Digital Twin facilitates the real-time collection of data and the execution of continuous simulations, enabling the identification of new areas for improvement. This approach to continuous improvement ensures that operational efficiency is maintained and enhanced over time. The application of machine learning and advanced data analysis technologies enables the identification of emerging patterns and the formulation of recommendations for further corrective actions.
# Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre
## 3 Modeling Scenarios and Data Processing
### Forecasted Gridded Weather Data
#### 3.2.1 Hurricane Dolly (2008)  
Dolly made landfall as a category 1 hurricane on the Saffir-Simpson Hurricane Wind Scale at South Padre Island, Texas, with estimated maximum winds of 86 mph. The storm reached peak intensity at around 1400 UTC on 23 July, 4 h before landfall, centered less than 20 nautical miles east of the Rio Grande River. Part of Hurricane Dolly's track can be seen in Figure 2 below.
# Power Flow Calculation Methods for Power Systems with Novel Structure UPFC
## 1 Introduction
Theoretically, when the load injection model (or the \(\pi\) load injection model) is adopted, the relevant elements of the Jacobian matrix could be modified to improve the convergence characteristic, but in practice, for the convenience of program implementation, the modifications to the Jacobian matrix are usually ignored. Figure 1: The structures of unified power flow controller (UPFC) projects: (**a**) Schematic diagram of the conventional structure UPFC; (**b**) Schematic diagram of the UPFC project in Nanjing western grid; (**c**) Schematic diagram of the UPFC project in Suzhou southern grid. The above methods essentially solve the state variables of the UPFC and the external system separately, and thus belong to the category of the alternating iteration method. Different from these methods, the literature [17; 18; 19; 20] adopts the comprehensive model. The series and shunt sides of the UPFC are equivalent to a series circuit of a controllable voltage source and an equivalent impedance, respectively. The voltage magnitudes and phase angles of the controllable voltage sources are regarded as new state variables, and solved together with the rest state variables of the system. In general, no matter what model is used, the convergence characteristic of the power flow calculation is better when the unified iteration method is adopted. However, on the other hand, the alternating iterative method is more convenient and flexible in use, and requires fewer changes to traditional AC power flow calculation methods. Therefore, some articles try to integrate UPFC into some emerging methods to obtain the advantages of the above two methods. In [10], a new UPFC model based on current injection is developed to integrate the UPFC into the Newton-Raphson current injection load flow method (NR-CIM). In [21], the power/current injection mismatches (PCIM) load flow method is proposed, and the UPFC model is implemented in the developed load flow method. In addition to the research on power flow calculation methods and modeling methods, the constraints handling approach of the UPFC has been analyzed in [22; 23]. Although the above articles are mainly applicable to the UPFC of the conventional structure, and have not considered the novel structure UPFC, they still have reference value for the power flow calculation of novel structure UPFC. This paper deduces an equivalent model of the general structure UPFC, and proposes three power flow calculation methods suitable for different UPFC structures. The rest of this paper is organized as follows: the general structure UPFC and its equivalent model are presented in Section 2. Three power flow calculation methods are proposed in Section 3. The characteristics of these methods are validated by case studies in Section 4. At last, conclusions are drawn in Section 5.
# Drowsiness Detection in Drivers Using Facial Feature Analysis
###### Abstract  
Drowsiness has been recognized as a leading factor in road accidents worldwide. Despite considerable research in this area, this paper aims to improve the precision of drowsiness detection specifically for long-haul travel by employing the Dlib-based facial feature detection algorithm. This study proposes two algorithms: a static and adaptive frame threshold. Both approaches utilize eye closure ratio (ECR) and mouth aperture ratio (MAR) parameters to determine the driver's level of drowsiness. The static threshold method issues a warning when the ECR and/or MAR values reach specific thresholds. In this method, the ECR threshold is established at 0.15 and the MAR threshold at 0.4. The static threshold method demonstrated an accuracy of 89.4% and a sensitivity of 96.5% using 1000 images. The adaptive frame threshold algorithm uses a counter to monitor the number of consecutive frames that meet the drowsiness criteria before triggering a warning. Additionally, the number of consecutive frames required is adjusted dynamically over time to enhance detection accuracy and more accurately indicate a state of drowsiness. The adaptive frame threshold algorithm was tested using four 30 min videos, from a publicly available dataset achieving a maximum accuracy of 98.2% and a sensitivity of 64.3% with 500 images.  
d  
t +
Footnote †: journal: applied sciences
## 2 Materials and Methods
### Electron Relaxation Model  
In this section, we present our model for hot carriers in order to explain the dynamics of the electronic response of optically excited graphene. The goal is to determine the dynamics of the transient electron temperature and quasi-Fermi levels of the hot carriers and, finally, graphene's optical conductivity in terms of appropriate parameters, such as characteristic relaxation times.  
Let us consider a pump beam consisting of photons with energy \(\hbar\omega_{b}\), inducing a non-thermalized population of electrons (holes) in the conduction (valence) band. The beam is focused on a circular spot on the graphene surface, where photocarriers are generated. We break down the hot carrier dynamics into two stages. The first stage spans the first few tens  
Figure 1: Carrier relaxation processes in graphene under ultrafast optical excitation. (**a**) Initially, graphene is in equilibrium. When the pump arrives (blue arrow), spectrally narrow populations of electrons and holes are created. These electrons and holes have non-Fermi-like distributions. (**b**) The electrons and holes undergo carrier-carrier scattering on the 10 fs scale so that two Fermi-like distributions are formed, one for electrons and another for holes, with different chemical potentials but equal temperatures. Panels (**c**–**e**) show different channels within the 100 fs timescale, through which electrons and holes recombine and thermalize: (**c**) Auger recombination. (**d**) Electron–hole recombination and carrier relaxation via optical phonon emission (dashed arrows). (**e**) Electron–hole recombination via plasmon emission (yellow solid arrow). (**f**) Further cooling is assisted by acoustic phonon emission (dashed arrow).  
of fs with carrier-carrier collisions, which is faster than the pump duration (\(\tau_{b}\approx 100\) fs) and drives the system toward a quasi-equilibrium distribution, where the charge carriers have a very high temperature compared to that of the lattice. The second stage, on the scale of a few hundred fs, occurs when the relaxation of carrier energy and electron-hole recombination take place. To model these processes, we introduce three adjustable parameters:  
1. Electron-electron (as well as hole-hole and electron-hole) scattering time, \(\tau_{ee}\);
2. Energy intraband relaxation time, \(\tau_{e}\), due to the electron scattering by lattice phonons, which is of the order of picoseconds, slightly exceeding the momentum relaxation time;
3. Interband recombination time, \(\tau_{r}\), determined by Auger recombination or via optical phonon or plasmon emission.  
These parameters satisfy the following relation [37]:  
\[\tau_{ee}\ll\tau_{e}\ll\tau_{r}\,. \tag{1}\]
## 3 Results
### Changes in Spatial Pattern of the Forest Cover in LP from 2007 to 2017  
The spatial distribution of forest cover in the Loess Plateau from 2007 to 2017 is shown in Figure 12. We can see that the forest and non-forest areas have changed from 2007 to 2017, specifically, the forest area grew while the non-forest area decreased.  
The land cover changes can be divided into four types as shown in Figure 13: unchanged non-forest areas in grey, non-forest areas to forest areas in red, forest areas to non-forest areas in blue, and unchanged forest areas in green (Figure 13a). The unchanged non-forest areas mainly concentrated in the northwest of the Loess Plateau, including Inner Mongolia, Gansu, Qinghai, Ningxia, the northern regions of Shaanxi and Shanxi which are adjacent to Inner Mongolia (Figure 13b). These areas with high altitudes and slopes did not change much and remained in non-forest areas.  
Figure 11: Forest variation in the forest cover in the Loess Plateau and the trend of the forest variation cover from 2007 to 2017.  
Figure 12: (**a–g**) Spatial distribution of forest cover in the Loess Plateau from 2007 to 2017 excluding 2011–2014 when the PALSAR data were unavailable.  
The unchanged forest areas (Figure 13c) were mainly distributed in the southeastern part of the Loess Plateau, concentrated in the central part of Shanxi Province and Shaanxi Province, and partially distributed in the southern edge of the Loess Plateau. The forests in the central region of Shaanxi and Shanxi grew because of the protection policy while the forests in the southern edge of the Loess Plateau remained stable due to the suitable environment and relatively low human disturbances.  
The conversion from forest to non-forest areas (Figure 13d) was mainly concentrated in the southern part of the Shaanxi Province, like Hu County, Xingping County, and Liquan County. The conversion from non-forest to forest areas (Figure 13e) was mainly distributed in the central part of Shaanxi Province, such as Zhidan County, Ansai County, Ganquan County, and the central part of Shanxi Province, like Linxian County, Fangshan County, and Lishi County.  
Figure 13: Spatial pattern of forest changes in the Loess Plateau (**a**). Unchanged areas of Loess Plateau including unchanged non-forest area (**b**) and unchanged forest area (**c**). The forest changes including conversion from forests to non-forest (deforestation) (**d**) and conversion from non-forest to forests (afforestation) (**e**).
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 4 Discussions and Conclusions
### Limitations and Future Work  
Despite the significant improvements, the proposed control method has certain limitations. One major limitation is the dependence of agent training on the accuracy of thetraining environment. The simulated engine model within the training environment may not fully reflect the real operating conditions of the engine, and conducting real engine training is both costly and hazardous. Additionally, the control effect of the thrust distribution agent is challenging to optimize, often failing to achieve perfect coordination between pressure and thrust. The reward function struggles to balance these two aspects effectively.  
To overcome these limitations, future work will focus on developing a semi-physical simulation system capable of realistically mimicking SADPS. This system aims to provide a reusable and realistic training environment for the algorithm. For thrust distribution, we plan to introduce global constraints into the RL framework, such as minimizing propellant consumption, maximizing total impulse, ensuring safe control, minimizing valve core oscillations, and maintaining constant openings for orbital and attitude control. These constraints will help the RL agents converge more effectively, achieving better coordination between pressure and thrust.
# VLSI-Friendly Filtering Algorithms for Deep Neural Networks
## 4 Minimal Filtering Algorithms
### Algorithm 1, \(M=3\)  
Let \(\textbf{X}_{5\times 1}=[x_{0},x_{1},x_{2},x_{3},x_{4}]^{\text{T}}\) be a vector that represents the input data set, \(\textbf{W}_{3\times 1}=\left[w_{0}^{(3)},\ w_{1}^{(3)},w_{2}^{(3)}\right]^{ \text{T}}\) be a vector that contains the coefficients of the impulse response of a 3-tap FIR filter, and \(\textbf{Y}_{3\times 1}=\left[y_{0}^{(3)},y_{1}^{(3)},y_{2}^{(3)}\right]^{ \text{T}}\) be a vector describing the results of using a 3-tap FIR filter:  
\[\textbf{Y}_{3\times 1}=\left[\begin{array}{cccc}x_{0}&x_{1}&x_{2}\\ x_{1}&x_{2}&x_{3}\\ x_{2}&x_{3}&x_{4}\end{array}\right]\left[\begin{array}{c}w_{0}^{(3)}\\ w_{1}^{(3)}\\ w_{2}^{(3)}\end{array}\right] \tag{8}\]  
As can be seen, calculating the product (8) requires \(9\) multiplications and \(6\) additions.  
We can formulate a streamlined algorithm for computing \(\textbf{Y}_{3\times 1}\) by utilising the following matrix-vector calculation procedure:  
\[\textbf{Y}_{3\times 1}=\textbf{T}_{3\times 6}^{(3)}\textbf{D}_{6}^{(3)}\textbf{T}_ {6\times 5}^{(3)}\textbf{X}_{5\times 1} \tag{9}\]where  
\[\textbf{T}_{3\times 6}^{(3)}=\left[\begin{array}{ccccc}1&1&\vdots&1&&\\ &1&1&\vdots&1&\\ &&\vdots&1&1&1\end{array}\right],\]  
\[\textbf{T}_{6\times 5}^{(3)}=\left[\begin{array}{ccccc}1&-1&-1&&\\ &1&&\\ &\cdots&-1&1&-1&\\ &&1&\cdots&-1&\cdots\\ &&1&\\ &&-1&-1&1\end{array}\right],\]  
and  
\[\textbf{D}_{6}^{(3)}=diag\Big{(}s_{0}^{(3)},s_{1}^{(3)},\ldots,s_{5}^{(3)} \Big{)},\]  
\[s_{0}^{(3)},=w_{0}^{(3)},\quad s_{1}^{(3)},=w_{0}^{(3)}+w_{1}^{(3)},\quad s_{2 }^{(3)},=w_{1}^{(3)}\]  
\[s_{3}^{(3)}=w_{0}^{(3)}+w_{2}^{(3)},\quad s_{4}^{(3)}=w_{1}^{(3)}+w_{2}^{(3)}, \quad s_{5}^{(3)}=w_{2}^{(3)}.\]  
Figure 2 depicts a data flow graph of the proposed algorithm for implementing the basic filtering operation for a 3-tap FIR filter. In this paper, the data flow diagrams are arranged from left to right, and straight lines in the figures represent data transfer operations. The circles in these figures indicate multiplication operations, with the corresponding numbers written inside the circles. The convergence points of the lines indicate summation, while dashed lines represent data transfer operations with a simultaneous change of sign. To maintain clarity, the figures utilise simple lines without arrows. Furthermore, to simplify the presentation, the superscripts of variables have been omitted in all figures, as the vector sizes involved in each case can be inferred from the figures themselves.
# Two-Dimensional Full-Waveform Joint Inversion of Surface Waves Using Phases and Z/H Ratios
## 5 Discussion
Thus, the requirements for the linear profile need to be considered before a field experiment is conducted. In addition, the real data FWI is indeed more difficult in near-surface application because of many practical problems, such as waveform data-signal processing problems in signal-to-noise ratio improvement, the effect of rugged topography beneath real irregular receiver array acquisition, a very complicated near-surface effect, the random or man-made noise effect, and heterogeneous distribution of ambient-noise sources. Ignoring any issue in the real data waveform inversion could cause the inversion result to be unexplained or unstable. This paper focused on the theoretical \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Inversion** & **Phase Inversion** & **Z/H Ratio Inversion** & **Joint Inversion** \\ \hline Model size & & x-direction: 600 m; z-direction: 100 m \\ (Nx \(*\) Nz) & & Nx = 100; Nz = 20 \\ \hline Propagation time & & 20,000 time steps; Time discretization: 0.0003 s \\ \hline Number of processors & & 4 & 4 & 8 \\ for each source & & & \\ \hline Total number of & & & \\ CPUs/cores & 200 & 200 & 400 \\ \hline Number of iterations & 46 & 54 & 38 \\ \hline Time for inversion & 6.25 h & 6.72 h & 5.80 h \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of numerical modeling and computational time for inversion. and technical aspects, and the real data application will be discussed and published in future works.
## 2 Materials and Methods
### Television Background
#### 2.1.2 Category Rating  
Absolute category rating (ACR) is a judgment that can be used to estimate the subjective quality of audio and video content. The impairment of service level might be expressed in the degradation category rating (DCR) (Table 1). ITU-T proposed that the grading scales assess the subjective level of perceived video and audio quality. However, listed gradations can be adopted for data-driven objective assessments [25]. The highest ACR grade describes excellent service quality, while the lowest means the perceived quality of content is terrible. The highest DCR grade assumes that the degradation is imperceptible, and the lowest impairment reaches annoying levels.