esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment Descriptions
### Study Domain and Model Configuration  
Two model domains, domain-1 (\(\sim\)0.23\({}^{\circ}\) horizontal resolution) and domain-2 (\(\sim\)0.05\({}^{\circ}\) horizontal resolution) were established (Figure 2). Domain-1 was used to downscale the initial and lateral boundary conditions derived from NCEP-FNL by ARPS to domain-2. Then, ARPS, LDAS-A, and CALDAS were applied to domain-2 independently. The physical parameterization options were configured with a 1.5-order turbulent kinetic-energy-based closure scheme for sub-grid-scale turbulent mixing, latitude-dependent Coriolis parameters, Kain and Fritsch cumulus parameterization [25] (only for domain-1), and Lin ice microphysics.  
Because integrated values of moisture fields were assimilated by CALDAS, the assimilated values were distributed with predefined profiles. Cloud Liquid Water Content (CLWC) was assumed to have a parabolic distribution (single-layer cloud) with zero values both above the top and below the bottom of the cloud layers. The lower and upper bounds of integrated CLWC were set to 0 and 1.5 kg\(\cdot\)m\({}^{-2}\), respectively.  
In this study, the assimilation of the 23-GHz channel for water vapor was not performed because such assimilation was found to cause errors (_i.e._, underestimation) in the assimilated water vapor profile. This underestimation in turn caused assimilated clouds to evaporate quickly to compensate for the water vapor deficiency (not shown). To avoid this particular problem, air was assumed saturated only within the cloud layer. However, more accurate information on the water vapor profiles would improve the development of convective systems in the model and thus, enhance rainfall prediction. Therefore, we are investigating the use of water vapor profile information within the CALDAS system, obtained from the AMSU and Atmospheric Infrared Sounder (AIRS).  
The distributions of rain and snow followed a skewed profile. They began to form at the cloud tops, grew to their maximum at the cloud bottoms, and subsequently decreased because of evaporation and the breakup of the raindrops or snowflakes. Further information on the model and the setup can be referenced from [12].
# Machine Learning-Based Paging Enhancement in 5G Network
## 4 Experiment and Results  
### Experiment Environment and Scenario  
For the performance evaluation of the probabilistic paging method applied with machine learning, the AMF system consisting of the UE classifier, probabilistic paging, and the 5G system implemented is shown in Figure 9.  
The experimental environment was developed by constructing a VM-based 5G simulator using scripts such as periodic or mobility registration update procedures, service request procedures, and paging processes.  
In addition, as shown in Table 6 and Figure 10, UE movement data of approximately 200,000 UE and approximately 1300 gNodeBs were obtained from a commercial MME system for one week. Subsequently, they were fit and adjusted for the experimental environment of the standard 5G specification. In addition, the KNN-supervised learning-based  
Figure 9: Experiment environment for proposed paging.  
UE classifier model was conducted using the UE profile information of the 1000 subscribers. The UE group of subscribers were classified into UE group 1, which included local residents and office workers who moved less between gNodeBs, and the remaining were classified as UE group 2. The settings for all the gNodeBs and UE were organized in the order of time in the 5G simulator, whereas the gNodeB list and transition probability based on the proposed method were updated every 20 min.  
Figure 11 shows the experimental scenario. When the UE sent a periodic or mobility registration and service request message to the AMF, the AMF stored the UE movement data in the gNodeB DB, generated the UE classifier model, and updated the machine learning paging table. When the SMF sent a service request message to the AMF, the AMF verified the UE state and performed probabilistic paging with machine learning.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**UE** & **RRC Establishment Cause** & **TAI** & **gNodeB** & **gNodeB** & **Time Stamp** \\  & & & **(OLD)** & **(NEW)** & \\ \hline
007361901 & Mo-Data & 23 & 983 & 473 & 06–16 \\
007369916 & Mt-Access & 23 & 983 & 303 & 06–16 \\
00776391 & Mo-Signaling & 87 & 983 & 953 & 06–16 \\  & & & & & 08:10:49 \\ \(\cdots\) & & & & & \\
007869871 & Mo-Data & 37 & 983 & 729 & 06–16 \\  & & & & & 08:10:56 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Example of UE movement data. Mo-data: mobile originating-data, mt-access: mobile terminating-access, and mo-signaling: mobile originating-signaling.  
Figure 10: Example of UE movement path from gNodeB (OLD).
## 4 Experimental Results
### Ablation Analysis of the Log-DRSN
Removing RSN unit-2 caused a greater performance loss (1.46% drop) compared to unit-1, indicating a more important role in preserving discriminative features. However, its removal also resulted in a moderate 14% reduction in computation time, suggesting a balance between performance and efficiency. * When the third unit of the RSN module was removed from the model, the accuracy rate of the model decreased the most when compared to the other two units. The calculation time has also been reduced the greatest with this choice. The accuracy rate was 96.40%, and the calculation time was 580.0 s. RSN unit-3 appears to be the most critical among the RSN components, with its removal resulting in a 2.6% accuracy reduction, the largest among RSN parts. At the same time, it yielded a 17.7% drop-in computation time, highlighting its computational load and its high influence on predictive power. * The removal of the ST module from the model resulted in the least change in the model's accuracy and complexity. The calculation time was 705.0 s, and the accuracy rate was 98.75. The negligible change in accuracy (<1%) and computation time indicates that the ST module has limited impact on overall model performance in this setup. This suggests that it may be optional or could be optimized further to justify its inclusion. * When all alternatives are considered, the performance difference from greatest to smallest is Attention, RSN-3, RSN-2, RSN-1, and ST modules, in that order. This ranking aligns with quantitative findings, where the attention module alone accounts for the majority of accuracy loss (over 70%), while the RSN units show a graduated impact. The ST module's limited effect confirms its position as the least critical component in terms of contribution to performance.
# Experimental Study and Numerical Simulation of W/O Emulsion in Developing Heavy Oil Reservoirs
## 4 Numerical Simulation  
In order to study the effect of emulsion flooding in heavy oil reservoirs, a numerical simulation was carried out using CMG software. In the process of the simulation, there were certain assumptions to ensure the stability of the emulsion system: (1) the reservoir was at a constant temperature, i.e., percolation occurred under isothermal conditions; (2) there was basically no material transfer between phases; (3) there was only one type of emulsion at any time; (4) there was almost no mass transfer between components.  
Table 4 lists the key parameters of the reservoir. As shown in Figure 23, part of the model was taken from the workspace of 48 \(\times\) 119 \(\times\) 46 = 262,752 grids. The model used in this paper had 11,250 valid grids. Furthermore, the block sizes of DX, DY, and DZ were 10 m, 10 m, and 2 m, respectively. In addition, Figure 24 displays the relative permeability curves. The development effect of waterflooding and emulsion flooding was compared under the same injection-production parameters and reservoir conditions.  
Figure 25a shows the change in water cut of waterflooding and emulsion flooding with time. Figure 25a reveals that the water cut growth rate of emulsion flooding slowed down when the water cut was between 40% and 60%, indicating that the emulsion played a role in plugging the high-permeability channel during this period, as seen in previous experimental results. Figure 25b shows the recovery of water flooding and emulsion flooding, where it can be seen that the oil recovery of emulsion flooding was 13% higher than that of waterflooding.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Value** & **Item** & **Value** \\ \hline Reservoir buried depth (m) & 1800 & Porosity (\%) & 21.08 \\ Thickness (m) & 21 & Oil saturation (\%) & 60.1 \\ Formation temperature (\({}^{\circ}\)C) & 54 & Permeability (mD) & 63.9 \\ Oil viscosity (mPa-s) (50 \({}^{\circ}\)C) & 1231 & Permeability variation coefficient & 0.58 \\ Oil density (kg/m\({}^{3}\)) & 0.92 & Rock compressibility (1/MPa) & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The key parameters of the reservoir.  
Figure 22: The relationship between water cut and oil displacement efficiency under different water cuts of emulsion.
# Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension
## 2 System Model
### Active Suspension Model  
In this paper, a two-DOF quarter active suspension system model [31] is considered. Moreover, the model in this paper is shown in Figure 3. It assumed that the tire always keeps contact with the road.  
In this model, \(m_{s}\) and \(m_{u}\) are the mass of sprung and un-sprung; \(x_{s}\) and \(x_{u}\) represent the displacement of the corresponding masses; \(k_{s}\) and \(k_{t}\) represent the stiffness of the vehicle suspension system and tire; \(u(t)\) denotes the active control force; \(c_{s}\) is the damper coefficient.  
Therefore, the dynamic mathematical model of a quarter car is as follows.  
\[\begin{split} m_{s}\ddot{x_{s}}(t)=k_{s}(x_{u}(t)-x_{s}(t))+c_{ s}(\dot{x_{u}}(t)-\dot{x_{s}}(t))+u(t)\\ m_{u}\ddot{x_{u}}(t)=k_{t}(x_{\bar{g}}(t)-x_{u}(t))-k_{s}(x_{u}(t)-x _{s}(t))-c_{s}(\dot{x_{u}}(t)-\dot{x_{s}}(t))-u(t)\end{split} \tag{2}\]  
Define the following state variable. \(x_{1}=x_{s}-x_{u}\) is the suspension dynamic deflection: \(x_{2}=x_{s}\) represents the sprung mass velocity; \(x_{3}=x_{g}-x_{u}\) denotes the tire deflection; \(x_{4}=x_{u}\) is the velocity of the un-sprung mass; \(W=x_{\bar{g}}\) is selected as the disturbance input caused by road roughness.  
The state vector is expressed as follows.  
\[X=\begin{bmatrix}x_{1}&x_{2}&x_{3}&x_{4}\end{bmatrix}^{T} \tag{3}\]  
Define the following output state variables, which are selected as follows.  
\[Y=\begin{bmatrix}\ddot{x_{s}}&x_{s}-x_{u}&k_{t}\times(x_{\bar{g}}-x_{u})\end{bmatrix} ^{T} \tag{4}\]  
Therefore, the dynamical equation can be written in the following state space form.  
\[\begin{split}\dot{X}=AX(t)+BU(t)+FW(t)\\ Y(t)=CX(t)+DU(t)\end{split} \tag{5}\]  
\[\begin{split}\text{where}\ \ A&=\begin{bmatrix}0&1&0&-1\\ -k_{s}/m_{s}&-c_{s}/m_{s}&0&c_{s}/m_{s}\\ 0&0&0&-1\\ k_{s}/m_{u}&c_{s}/m_{s}&k_{t}/m_{u}&-c_{s}/m_{s}\end{bmatrix},\ \ B&= \begin{bmatrix}0\\ 1/m_{s}\\ 0\\ -1/m_{u}\end{bmatrix},\ \ F&=&\begin{bmatrix}0\\ 0\\ 1\\ 0\end{bmatrix},\\ C=\begin{bmatrix}-k_{s}/m_{s}&0&0&0\\ 1&0&0&0\\ 0&0&k_{t}&0\end{bmatrix},D=\begin{bmatrix}1/m_{s}&0&0\end{bmatrix}^{T},U= \begin{bmatrix}x_{\bar{g}}\end{bmatrix}.\end{split}\]  
Figure 3: The Quarter Car Active Suspension System.
## 2 Theoretical Background as the Basis for Model Development
### Production Planning and Control: Push Versus Pull  
One of the central tasks of production management is production planning [20] (p. 27). The original tasks of production planning include the planning of the products to be manufactured, as well as the required production factors and processes [21] (p. 29). Production management contains the tasks of design, planning, monitoring, and control of the productive system and business resources such as people, machines, material, and information [22] (pp. 249-273). In this context the task of production planning and control is the planning and control of deadlines, delivery dates, capacities and quantities of manufacturing, and assembly processes [21] (p. 29).  
The planning of production processes contains a comprehensive planning of delivery dates oriented to customer orders, a reservation planning of the capacities oriented to each of the machines (sequence planning) and a planning of the personnel and material to be available [23].  
The processes in a supply chain are divided into two categories depending on whether they are executed in response to a customer order or in anticipation of customer orders. Production according to the \"pull\" principle is initiated by customer orders, while the \"push\" principle is initiated and carried out in anticipation of customer orders and is usually based on demand forecasts. This dilemma is extremely useful when considered the strategic decisions related to the design of the supply chain, and therefore of the production [24] (p. 14). The objective of the design of a productive system within a supply chain is to find the balance between the two. Balance is described by the decoupling point of the customer order [25] (p. 52). An ETO manufacturer is a producer following mainly the pull principle across the supply chain with the decoupling point at the beginning of it or even not existing well pure pull is applied.  
In terms of the publication the concept of push and pull compared does not correspond to the nature of producing in response or anticipation of customer orders, but it deals with the regulation and control method applied in the production process in order to release orders into production in the different steps. A push control approach initiates production trying to maximize production utilization without considering stocks and the restrictions along the production process. On the other hand a pull approach is based on the TOC and considers the restrictions and stocks in the current and future state of the production system.
# Quantitative Electroencephalographic Analysis in Women with Migraine during the Luteal Phase
## 3 Data Analysis  
### EEG Spectral Power Analysis  
We performed offline visual artifact rejection to exclude contaminated segments. After that, we concatenated segments of 60 s (15,000 points) from each participant. We computed spectral power (SP) for the considered frequency bands by using customized software scripts programed in MATLAB (Mathworks, 2019), using the following formula:  
\[SP_{C}(f)=\frac{1}{n}\sum_{i=1}^{m}C_{i}(f)C_{i}^{*}(f)\]  
where \(C_{i}\) represents the Fourier-transformed channel \(c\) for a given segment number (\(i=1\), \(n\)) and \"*\" indicates the complex conjugate.  
We calculated the SP in the following frequencies: delta (0.5-3.5 Hz), theta (4-7.5 Hz), alpha (8-12 Hz), and beta (13-30 Hz). The frequency resolution we selected was 0.1 Hz.
# Map Matching Based on Seq2 Seq with Topology Information
## 4 Experiment
### Experimental Data  
The training data used for the model consisted of the road network of Yibin City downloaded from OpenStreetMap, where standard trajectory data were generated through simple path search in NetWork X, and simulated noisy trajectory data. A total of 260,389 standard paths and 13,019,450 simulated noisy paths were used.
## 2 Electroosmotic Peristaltic Carreau Rheological Model
### Rheological Carreau Fluid Model  
The Cauchy stress tensor for Carreau fluid model is [29]  
\[\overline{\tau}=\eta\Big{(}\overline{\dot{\gamma}}\Big{)}\overline{A}_{1}, \tag{2}\]  
with  
\[\eta\Big{(}\overline{\dot{\gamma}}\Big{)}=\eta_{\infty}+(\eta_{0}-\eta_{ \infty})\left[1+\Big{(}\overline{\dot{\gamma}}\Big{)}^{2}\right]^{\frac{( \overline{\eta}-1)}{2}}, \tag{3}\]  
\[\overline{A}_{1}=\Big{(}\
abla\overline{\dot{\psi}}\Big{)}+\Big{(}\
abla \overline{\dot{\psi}}\Big{)}^{\mathrm{T}},\,\text{where}\,\,\,\overline{ \dot{\psi}}=\!\Big{[}\overline{U}\Big{(}\overline{X},\overline{Y},\overline{t }\Big{)},\,\,\overline{\dot{\psi}}\Big{(}\overline{X},\overline{Y},\overline {t}\Big{)},\,\,0\big{]} \tag{4}\]  
\[\overline{\dot{\gamma}}=\sqrt{\frac{1}{2}\sum_{i}\sum_{j}\overline{\dot{ \gamma}_{i}\dot{\gamma}_{j}}\dot{\gamma}_{j}}=\sqrt{\frac{1}{2}\Pi}=\sqrt{ \frac{1}{2}\mathit{tr}\Big{(}\overline{A_{1}}^{2}\big{)}}, \tag{5}\]  
here \(\overline{\dot{\tau}},\eta\), \(\widetilde{\dot{\gamma}}\), \(\overline{A}_{1},\eta_{0},\,\,\eta_{\infty},n_{i},\,\,\Gamma\), \(\overline{\dot{\psi}}\) and \(\Pi\) are the Cauchy stress tensor, shear rate viscosity, shear rate, first Revilin Erickson tensor, zero shear rate viscosity, infinite shear rate viscosity, power law index, material time constant, velocity vector and second invariant tensor (strain rate tensor) respectively. The constitutive Equation (3) for \(\eta_{\infty}=0\), i.e.,  
\[\overline{\dot{\tau}}=\eta_{0}\Big{[}1+\Big{(}\overline{\dot{\gamma}}\Big{)} ^{2}\Big{]}^{(\frac{\overline{\eta}-1}{2})}\overline{A}_{1}. \tag{6}\]  
Note that the Carreau-model with power law index (fluid index \(n_{i}\)) expresses the following situations [30]:  
1. For \(n_{i}=1\) or \(\Gamma=0\), Carreau-model reduces to Newtonian fluids.
2. For \(0<n_{i}<1\), Carreau-model reduces to pseudoplastic fluids (shear thinning fluids).
3. For \(n_{i}>1\), Carreau-model reduces to dilatant fluids (shear thickening fluids).
# A Survey of Vehicle to Everything (V2X) Testing
## 3 V2X Network Challenges  
### Latency/Reliability Challenges  
The performance of the network is most important for applications which require low latency and high reliability. DSRC uses CSMA/CA to achieve collision avoidance and the ability for multi-user access. With fewer vehicles, DSRC has lower latency and higher reliability, but its performance is opposite in a dense vehicle environment. The latency of LTE-V2X is relatively stable, and the communication delay based on the PC5 interface, which can provide predictable delay and less interference is lower than 100 ms [28]. In the future, 5G networks will provide a communication delay of less than 1 ms while providing a stability of 99.999%, so they will be able support automatic-driving-oriented V2X services.  
The V2X network faces many kinds of attacks which could lead to reduced performance. Denial of service (DoS) or distributed DoS (DDoS) refer to intensive use denial attacks by internal or external attackers on target nodes, resulting in the exhaustion of network resources and service resources [29]. They can cause serious problems, such as high latency of communication networks, network unavailability, and unavailability of node services. Jamming attacks and greedy behavior attacks, are examples of DoS attacks [30]. A jamming attack is an attack on the physical layer. The attacker jams the wireless channel through electromagnetic interference, which increases the  
Figure 2: Vehicular Network Applications.  
latency of the V2X communication and reduces the network's reliability [31]. A greedy behavior attack refers to a network node that violates the rules of channel access and occupies too many channel resources, thereby reducing the performance of other nodes and causing network congestion [32].
## 3 Results and Discussion
The improved Mask2 Former model achieved higher accuracy and mean Intersection over Union (mIoU) scores, setting a benchmark for future studies in the field. Additionally, the automatic generation of the QAP diagram offers a valuable tool for geologists, enabling more efficient and precise mineral classification. Despite these advancements, certain limitations remain. The study's dataset is focused on igneous rocks, which may restrict the generalizability of the model to other rock types, such as sedimentary and metamorphic rocks. Furthermore, the computational cost associated with the refined Mask2 Former architecture presents a challenge for real-time applications and resource-constrained environments. Addressing these issues through model optimization and expanding the dataset to include a broader range of rock types could enhance the applicability and efficiency of the approach. Future research directions include the integration of hyperspectral imaging modalities to capture additional spectral information, further improving segmentation accuracy and mineral identification. Exploring lightweight architectures and model compression techniques may also facilitate real-time deployment in field settings. By addressing these aspects, the proposed approach has the potential to transform geological image analysis and contribute significantly to the fields of geology, mining, and environmental science.
## 1 Introduction  
With the development of the intelligent transportation system, the travel of residents is growing more convenient. Nevertheless, because of the information asymmetry between passengers and drivers, the spatial and temporal distribution of passengers and drivers are inconsistent. The limited urban transportation resources were wasted by the information asymmetry between passengers and drivers. Therefore, trip demand in the urban area urgently needs to be studied. Recently, online taxi-hailing has gradually become the primary trip mode for urban residents. Meanwhile, the taxi still assumes the function of public transportation for urban residents. Under these circumstances, the online taxi-hailing demand would be affected by the taxi demand because of the homogeneity between the taxi and online taxi-hailing. Thus, we should take the taxi demand into account while studying the online taxi-hailing demand.  
In the past, research that focused on forecasting traffic demand was mostly based on environmental data and GPS data [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. Moreover, the research mined the features of GPS data and environmental data to forecast the trip demand, while the research ignored the relationship between the taxi and online taxi-hailing.  
Therefore, this study aims to enhance the prediction effects of forecasting online taxi-hailing demand considering the taxi demand. Moreover, this research is a follow-up experiment of [32]. First, we use Pearson correlation analysis to screen the determinative influence factors to enhance the prediction accuracy. Then, online taxi-hailing demand forecasting models based on extreme gradient boosting (XGB) and backpropagation neural network (BPNN) were introduced to explore the relationship between taxi demand and online taxi-hailing demand. Next, we realize the real-time forecasting of online taxi-hailing demand by proposing a data-driven prediction method. This study would help to enhance the accuracy of online taxi-hailing demand forecasting and is essential for rebalancing traffic resources.  
The literature review related to our study is presented in Section 2. Section 3 describes the data and the preprocessing of data in this study. Next, we proposed methods to enhance the accuracy of predicting online taxi-hailing demand in Section 4, while Section 5 concludes the results. Finally, the discussion and conclusion are shown in Section 6.