esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling the 2013 Zika Outbreak in French Polynesia: Intervention Strategies
## 1 Introduction
The human travel patterns have not been taken into account in the existing models for studying the transmission dynamics for Zika in French Polynesia [6; 7]. The transit network for the 23 islands is used to transport people from one island to another on each day. Because of the short life span of mosquitoes, they are assumed not to recover from the disease. Mosquitoes fly over very short distances, therefore migration of mosquitoes from one island to another is not taken into account. There is no infection induced mortality for humans or mosquitoes. Humans are long lived compared to the duration of the outbreak and the life span of mosquitoes therefore human mortality and birth rates are ignored. Humans are assumed to have lifelong immunity after being infected once [8; 9]. All humans are assumed to be susceptible to the disease at the start of the outbreak because of the non-availability of any vaccine. Despite the possibility of sexual transmission of Zika, it is ignored in this research because it is difficult to model sexual transmission of any disease unless the sexual contact network is known for the population and assuming homogeneous random sexual contacts between individuals is not reflective of real life human behavior. Furthermore, research conducted by [PERSON], [PERSON] et al. in [10] has shown that sexual transmission contributes only about 3% to the basic reproduction number for Zika. Additionally, the possibility of vertical transmission of Zika in mosquitoes is still being explored [11; 12; 13] and is therefore not considered to be a factor in this research. Additionally, we explored the effect of implementing intervention measures to control the mosquito population, or to prevent mosquito bites, or to restrict travel to infected islands. It was seen that reducing the mosquito population and preventing mosquito bites led to a significant decrease in the size of the outbreak, but imposing travel bans after the start of the outbreak did not change the outcome unless islands were totally isolated before the infection reached them. It is seen that proactive mosquito control measures addressing both reducing the size of the vector population and preventing contact mosquito-human contact are significantly more effective than controlling human travel patterns. In the next section, we discuss research that has been conducted in the area of vector borne disease modeling. Section 3 describes the system and all the parameters associated with each model in detail. The computation of the basic reproduction number is also discussed at the end of Section 3. The experiments and results are explained in Section 4 and we conclude the paper in Section 5.
# Smart Grid Protection, Automation and Control: Challenges and Opportunities
## 3 Evolution of Substation Architectures and Digital Modernizations for Smart Substations
### Migration from Conventional to Digital Substations
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Step** & **Description** & **Tasks** & **Benefits** \\ \hline \multirow{2}{*}{1} & Conventional Substations— Protection and Control & Existing state with copper wiring and conventional relays. & Existing challenges with high costs, long installation, and low flexibility. \\ \hline \multirow{6}{*}{2} & & & Faster deployment: Quicker installation process due to reduced physical wiring. \\  & & & Monitored communication: Real-time monitoring of communication channels. \\  & Digital Substation “Light” & Replace copper hardwiring with fiber optic (FO)-based IEC & Cost reduction: Lower costs associated with reduced copper wiring. \\  & Hardwiring Replaced by Process Bus FOPtection and Control & 61850 Process Bus (61850-9-2 LE). & Interoperability: Enhanced compatibility with various devices and manufacturers. \\  & Bus FOPtection and Control & Implement IEC 61850 GOOSE messaging for communication. & - Monitored communication. \\  & & & Cost reduction. \\  & & & Improved interoperability. \\ \hline \multirow{6}{*}{3} & Digital Substation “FULL” & Implement a full process bus FO infrastructure. & Simplification: Reduced complexity with centralized control. Space savings: Less physical space required for equipment. Enhanced flexibility: Easier to add new functions through software updates. \\  & Process Bus FO + Centralized PACS Backups/ & & \\  & Faults + Anomalies Recordings & & \\ \hline \multirow{6}{*}{5} & Digital Substation “FULL” & Integrate backup systems and fault/anomalies recording capabilities within the centralized PACS. & Improved reliability: Enhanced fault detection and recording for better analysis. Data management Centralized storage and management of fault and anomaly data. \\  & Digital Substation “FULL” & Implement AI-based fault analysis within the centralized PACS. & Advanced diagnostics: AI algorithms provide detailed fault analysis, improving maintenance and response times. Predictive maintenance: Early detection of potential issues before they cause significant problems. \\ \hline \multirow{6}{*}{6} & Digital Substation “FULL” & & Proactive maintenance: AI predicts faults before they occur, allowing for proactive maintenance. \\  & Process Bus FO + Centralized PACS AI Fault Analysis + AI Fault Prediction & & Operational efficiency: Reduced downtime and maintenance costs through predictive insights. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Steps to migration from conventional to digital substation. vanced safety technologies are essential for protecting personnel and equipment. CPAC systems enhance safety through remote operations, non-conventional transformers, and arc sensors. These technologies improve accuracy and response speed, and reduce hazard exposure. Table 7 highlights key aspects and benefits of these advancements in modern electrical safety.
# Dynamic Modeling and Full-Process Simulation of the Core Engine Test Rig Main Test System
## 3 Modularization Modeling Approach
### Gas Valve Model  
#### 3.1.1 Finite Volume Model  
For the components with small length-to-diameter ratio and sudden change in the cross section in the fluid flow direction, such as stop valve, ball valve, compensator, charge/discharge valve, safety valve, and orifice plate, the key issue to be addressed in the modeling is how to describe the throttling and switching characteristic of the valve spool.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module Type Name** & **Type Code** & **Identifier** & **Module Attribute** \\ \hline Fluid Source & 00 & FS & Basis \\ Gas Pipe & 02 & GP & Basis \\ Heater Exchange Cold Gas & 05 & HECG & Combination \\ Gas Volume & 12 & GVol & Basis \\ Gas Mixing Apparatus & 17 & GMA & Basis \\ Gas Steady Apparatus & 20 & GSA & Combination \\ Gas Valve & 22 & GV & Basis \\ \hline \hline \end{tabular}
\end{table}
Table 1: Modularization disassembly of the core engine test rig main test system.  
Assumptions:  
(1) The gas is 1D ideal gas flow without considering the effects of axial heat conduction and gravity field.  
(2) The state parameters, such as pressure, density, and total energy per unit volume in the gas volume unit, are instantaneously uniform and homogeneous, and the frictional and local losses are ignored.  
(3) The wall is a rigid wall, irrespective of the wall elastic deformation.  
(4) The valve-spool throttling can be described by the valve mass-flow-rate algebraic equation \(Q_{\text{m}}=f(\tau,p_{1},\rho_{1},p_{2},\rho_{2})\) (actually can be used only when \(\tau A_{\text{vt}}\ll A_{\text{v1}},A_{\text{v2}}\)).  
(5) The local loss of the spool throttling can be described by introducing a flow coefficient.  
As shown in Figure 2, the half elements of the upstream and downstream of the spool are considered as two velocity elements, \(u_{1}\) and \(u_{2}\), and their model equations are as follows:  
Continuity equation:  
\[\left\{\begin{array}{c}\frac{\text{d}\rho_{1}}{\text{d}I}=\frac{1}{V_{1}} \big{(}\rho_{1}^{\text{in}}u_{n}A_{n}-Q_{\text{m}}\big{)}\\ \frac{\text{d}\rho_{2}}{\text{d}I}=\frac{1}{V_{2}}\big{(}Q_{\text{m}}-\rho_{2 }^{\text{out}}u_{0}A_{0}\big{)}\end{array}\right. \tag{1}\]  
Energy equation:  
\[\left\{\begin{array}{c}\frac{\text{d}E_{1}}{\text{d}I}=\frac{1}{V_{1}} \Big{[}\big{(}E_{1}^{\text{in}}+p_{1}^{\text{in}}\big{)}u_{n}A_{n}-\big{(}E_{1 }^{\text{out}}+p_{1}^{\text{out}}\big{)}\frac{Q_{\text{m}}}{\rho_{1}^{\text{ out}}}-\dot{q}_{1}S_{1}\Big{]}\\ \frac{\text{d}E_{2}}{\text{d}I}=\frac{1}{V_{2}}\Big{[}\big{(}E_{2}^{\text{in }}+p_{2}^{\text{in}}\big{)}\frac{Q_{\text{m}}}{\rho_{2}^{\text{in}}}-\big{(}E _{2}^{\text{out}}+p_{2}^{\text{out}}\big{)}u_{0}A_{0}-\dot{q}_{2}S_{2}\Big{]} \end{array}\right. \tag{2}\]  
For the wall of the gas valve inlet channel (inlet chamber) and outlet channel (outlet chamber), one of the radial one-dimensional heat transfer model, the zero-dimensional heat transfer model, and the adiabatic model [15; 16] can be selected based on the actual condition of the temperature field.
## 1 Introduction
An online label-smoothing training strategy was also employed to reduce overfitting and improve classification performance. The methods discussed above have spurred notable progress in mechanical equipment health state identification. However, they still exhibit several limitations. First, the construction of feature sets using single-modal data results in information loss, hindering the comprehensive extraction of relevant equipment characteristics. Additionally, the characteristics of different modal data can vary significantly, and existing state feature extraction methods fail to effectively capture the correlations between multiple modalities. To address the aforementioned issues, the objective of this study is to design a deep learning model that integrates multimodal features, fully leveraging the complementary characteristics among modalities to enhance the accuracy and robustness of rotating machinery health state recognition. To achieve this objective, the following three specific research tasks were undertaken:  
1. Construction of a multimodal feature set: to address the heterogeneity of different modal data, we integrated multiple source signals (e.g., vibration signals, temperature signals) to construct a comprehensive and representative multimodal feature set for equipment condition characterization. 2. Design of a joint representation learning layer: Considering the complementarity and consistency among modalities, we designed a joint representation learning layer that combines orthogonal projection with a Transformer-based architecture. This enables the joint learning of multimodal features within a shared subspace, resulting in more generalizable and discriminative feature representations. 3. Development of a health state assessment model: we proposed a health state assessment model based on a ResNet, which effectively utilizes deep feature information to achieve the efficient and accurate recognition of equipment health states. Multimodal Joint Representation Learning and Residual Neural Network-Based Health Status Recognition Method  
Our goal is to obtain effective feature representations for health state recognition using vibration, sound, and image modal information. Initially, we extract features from vibration, sound, and image data using a GAF, MFCCs, and a Faster RCNN, respectively, to construct a comprehensive feature set. Considering the complementarity and consistency between modalities, we propose a joint representation learning layer that employs orthogonal projection in combination with the Transformer model. This approach enables the learning of more generalized feature representations within a shared subspace. Finally, the extracted features are input into a ResNet model for health state recognition. The overall architecture, as illustrated in Figure 1, consists of three main components: feature set construction, multimodal joint representation learning, and health state evaluation based on the residual neural network.
# Modified Smith Input-Shaper Crane-Controller for Position Control and Sway Reduction
## 5 Conclusions  
Implementing input shaping within the control loop, particularly within the position controller, significantly reduces the system's phase margin. This reduction occurs due to  
Figure 31: The quality of the positioning of the cart. Figure 32: Sway reduction of the cargo. the additional delay introduced by the input shaper, which can lead to oscillatory behavior and instability in the load positioning dynamics, potentially jeopardizing the system's accuracy and responsiveness. A Smith predictor was employed to mitigate these challenges and achieve satisfactory positioning performance while utilizing input shaping. This advanced control strategy was explicitly modified to address this application's unique requirements. The necessary adjustments to the Smith predictor were thoroughly documented, providing insights into the rationale behind the modifications and their anticipated effects on system stability and performance. In this paper, a series of experimental and simulation comparisons were conducted with a laboratory crane to evaluate the effectiveness of the proposed control structure. The performance of this structure was assessed against two benchmarks: one control structure that did not incorporate anti-swaying mechanisms and another that included such mechanisms. The data collected revealed that the proposed control structure successfully maintained the original parameters of the position controller while effectively counteracting the adverse effects of input shaping on positioning quality. Notably, it enhanced the anti-swaying capability by approximately 94% for the load and improved positional quality to 82%. This improvement culminated in achieving non-oscillatory position responses, as determined by the integral performance criteria. The integral performance criteria employed in the analysis measures the system's overall performance over time, allowing for a comprehensive assessment of both positional accuracy and sway reduction attributable to the proposed control architecture. Moreover, an essential advantage of the proposed structure lies in its straightforward implementation on microcomputers. The simplicity of this implementation process is a crucial factor that should instill confidence within the audience, empowering them to effectively apply the proposed control structure in the crane systems without significant barriers to entry. The following nomenclature is used in this manuscript:  
\(v_{r}\) speed of the cart \(m_{r}\) mass of the cart \(x\) position of the cart \(x_{r}\) reference position of the cart \(x_{m}\) measured position of the cart \(x_{s}\) simulated position of the cart \(D_{cuv}\) wheels diameter of the cart \(K_{sm}\) position gain of the cart \(\hat{x}\) estimated position of the cart
## The Methodology of Numerical Modeling
### Overview of the Models and Their Interactions
This initial tube temperature distribution provided the reference heat input from the burner for every element in the CHT model. The radiation model was used to calculate the elements' net heat fluxes for each discretized tube. The basis for this calculation was the temperature distribution on these tubes according to the particular setups, as well as the surface emissivities. An element's net heat flux is defined as the difference between the heat flux the element emits and the one the element receives. The calculation of the radiation exchange for a particular radiant tube setup is based on the principle of [PERSON]'s zonal method [37]. View factors based on the elements' geometrical arrangements towards each other are defined to evaluate the heat fluxes between all elements. For \(n\) elements in a radiant tube setup, the creation of an n x n view factor matrix is required. With the help of these view factors, the total radiation exchange between the tube and its surroundings can be calculated. Furthermore, it is to be noted that a shadow algorithm is necessary to identify shaded element pairs and adjust the view factor matrix accordingly. The radiation model was created using the numerical computing environment MATLAB(r). The conduction model's basis was an analysis with conduction as the only active heat transfer mechanism taking place inside the tubes. The model was called up individually for each tube with its specific net burner heat fluxes (radiation model) and the initial tube temperature distributions (FVM model). The tubes' material properties, as well as a tube-setup with no mechanical constraints or loads, completed the model's input parameters. With these boundary conditions, the model calculated the new temperature distributions for each tube. The coupling of the conduction and radiation model was formed through the feedback of the new temperatures obtained. In an iterative approach, the net heat fluxes and temperature distributions were updated until a steady-state solution was achieved, i.e., a given convergence criterion (change of elements' temperature \(<\) 0.0001 %) was reached. The final tube temperature distribution served as input to the FEM model. The export of temperature distribution from one model to the next was performed using the nearest distance interpolation on the respective computational nodes or elements.
## 3 Modelling of the Bus Transportation Network
### Bus Consumption Modelling
#### 3.2.2 Speed Profile  
The speed of the bus must be determined based on the distances and travel times between bus stops taken from the GTFS data in order to calculate the power consumption. Inspired by SORT standard driving cycles [48; 49; 50], the present paper considers a speed profile with a trapezoid shape between two consecutive bus stops, as seen on Figure 3.  
The trapezoid is defined by the maximal speed \(v_{max}\) and the acceleration \(a\). Travel time and the distance between two bus stops \(t_{bs}\) and \(d_{bs}\) are determined using GTFS data. The spatial and temporal constraints of the bus travel can be determined from the speed profile. The distance between the consecutive stops is therefore determined as follows:  
\[d_{bs}=2\cdot d_{var}+d_{cst} \tag{4}\]  
\[d_{var}=\int_{0}^{T_{var}}v(t)\,dt=\int_{0}^{T_{var}}a\cdot t\,dt=\frac{a}{2} \cdot T_{var}^{2} \tag{5}\]  
\[d_{cst}=\int_{T_{var}}^{T_{var}+T_{cst}}v(t)\,dt=v_{max}\cdot T_{cst} \tag{6}\]  
where \(d_{var}\) and \(d_{cst}\) are the distances travelled during the acceleration/deceleration and constant speed phases, respectively. \(T_{var}\) is the acceleration/deceleration time, \(T_{cst}\) is the time at constant speed, and \(v(t)\) is the bus speed. The time to travel between the two stops is described below:  
\[t_{bs}=2\cdot T_{var}+T_{cst} \tag{7}\]  
\[T_{var}=\frac{v_{max}}{a} \tag{8}\]  
Therefore, using Equations (7) and (8) with the value of discriminant of the quadratic equation \(\Delta=t_{bs}^{2}-\frac{4}{a}\cdot d_{bs}\), the maximal speed \(v_{max}\) is determined by  
\[\frac{v_{max}^{2}}{a}-t_{bs}\cdot v_{max}+d_{bs}=0 \tag{9}\]  
\[v_{max}=\left\{\begin{aligned} &\frac{t_{bs}-\sqrt{t_{bs}^{2}-\frac{4}{a} \cdot d_{bs}}}{3}\cdot a,&\text{if }\Delta>0\\ &\frac{t_{bs}}{2}\cdot a&\text{if }\Delta=0\end{aligned}\right. \tag{10}\]  
If a delay is anticipated for arriving to the next stop on the route, the EB is assumed to increase its speed and acceleration up to limits. If the maximal speed--computed based on the real departure time of the bus from one stop and the theoretical arrival time to another--is above the speed limit, the speed is capped to a maximal value \(v_{lim}\) and a new acceleration is computed. If the new acceleration is also above its limit, then it is capped to \(a_{lim}\) and the bus is considered to be late arriving to the next stop.  
Figure 3: Bus speed profile in trapeze representing the travel between two bus stops.
## Materials and Methods
### Data  
#### 2.2.1 Flux-Tower Data  
A total of 57 site-year data from 16 grassland sites during 2003-2013 were retrieved from the ChinaFLUX and the Coordinated Observations and Integrated Research over Arid and Semi-Arid China (COIAS) databases, including 43 site-years from 11 sites on the Tibetan Plateau and 14 site-years from 5 sites on the Inner Mongolia Plateau (Table 1). The observed eddy-covariance data were processed through a three-dimensional coordinate rotation, WPL (Webb-Pearman-Leuning) correction, and invalid data exclusion [34]. Subsequently, the missing nighttime \(RE\) and daytime \(RE\) data were calculated using the Lloyd-Taylor equation based on net ecosystem exchange (NEE) observations during the nighttime [35]. The entire procedure was completed using the ChinaFLUX CO\({}_{2}\) data processing system [36]. The half-hourly flux data was summed to obtain daily values, and the site-years with more than 30% of the daily \(RE\) missing were eliminated. To comply with the temporal scale of the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day composite imagery, the processed daily \(RE\) were averaged within the same periods.  
Figure 1: Map showing the spatial distribution of the alpine and temperate grasslands in China. Triangles denote the 16 flux-tower sites. DXST = DangXiong alpine meadow steppe; DXSW = DangXiong alpine swamp meadow; HBSW = HaiBei alpine swamp meadow; HBSH = HaiBei alpine shrub meadow; HBSH = HaiBei alpine _Kobresia_ meadow; MQSW = MaQu alpine swamp meadow; MQKO = MaQu alpine _Kobresia_ meadow; ZF = ZhuFeng; NMC = NaMuCuo; GL = GuoLuo; AR = ARou; NMG = NeiMengGu; XLHT = XiLinHaoTe; SZWQ = SiZiWangQi; HLBE = HuLunBeEr; DL = DuoLun.
###### Abstract  
With the rapid advancement of unmanned aerial vehicle technology, the extensive application of multiple unmanned aerial vehicle systems in agriculture has led to significant innovations and benefits. Addressing the challenge of task allocation for multiple unmanned aerial vehicles, the primary objective is to minimize the total time required for unmanned aerial vehicles to return to their starting point after task completion. To tackle this issue, a mathematical model for the multi-constrained multiple unmanned aerial vehicle collaborative task allocation problem is developed. To efficiently solve this model, we propose an enhanced Seagull Optimization Algorithm, which integrates the Tent chaotic mapping strategy and the Levy flight strategy. The Tent chaotic mapping helps the algorithm avoid becoming trapped in local optima, while the Levy flight strategy, employed during the seagull attack phase, enhances the algorithm's diversity and its ability to escape local optima. Additionally, the spiral coefficient is refined to balance the coordination between global and local searches. Simulation experiments demonstrate that the proposed algorithm can swiftly and effectively identify a reasonable task allocation scheme for solving the multi-constrained multi-UAV collaborative task allocation problem.  
multiple unmanned aerial vehicles; collaborative task allocation; tent chaotic mapping; Levy flight; seagull optimization algorithm +
Footnote †: journal: Applied  
Article  
A Tent-Levy-Based Seagull Optimization Algorithm for the Multi-UAV Collaborative Task Allocation Problem