esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Novel GVNS Versus Conventional GVNS
\begin{table}
\begin{tabular}{c c c c c} \hline
**Problem** & **Av. Value (nGVNS)** & **Best (GVNS)** & **Problem** & **Av. Value (nGVNS)** & **Best (GVNS)** \\ \hline bayg29 & 1610 & 1653 & br17 & 39 & 39 \\ \hline bays29 & 2020 & 2069 & ftv33 & 1357.8 & 1489 \\ \hline fri26 & 937 & 969 & ftv35 & 1544.6 & 1791 \\ \hline gr17 & 2085 & 2085 & ftv38 & 1616.6 & 1778 \\ \hline gr24 & 1272 & 1278 & ftv44 & 1763.8 & 2014 \\ \hline ulysses16 & 6859 & 6859 & p43 & 5554 & 5629 \\ \hline ulysses22 & 7013 & 7013 & ry48p & 14,698.2 & 15,134 \\ \hline gr48 & 5049.4 & 5325 & & & \\ \hline hk48 & 11,508.6 & 11,884 & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Data based on the novel GVNS and the conventional GVNS [35]. \begin{table}
\begin{tabular}{c c c c} \hline
**Benchmark** & **OV** & **GVNS FI** & **GVNS BI** \\ \hline br17 & 39 & 39 & 39 \\ \hline ft53 & 6905 & 7328 & 7135 \\ \hline ft70 & 38,673 & 40,691 & 40,206 \\ \hline ftv33 & 1286 & 1339 & 1286 \\ \hline ftv35 & 1473 & 1499 & 1473 \\ \hline ftv38 & 1530 & 1585 & 1541 \\ \hline ftv44 & 1613 & 1760 & 1644 \\ \hline ftv47 & 1778 & 1992 & 1816 \\ \hline ftv55 & 1608 & 1985 & 1665 \\ \hline ftv64 & 1839 & 2382 & 1986 \\ \hline ftv70 & 1950 & 2557 & 2157 \\ \hline frv170 & 2755 & 3923 & 3852 \\ \hline kro124p & 36,230 & 43,187 & 37,076 \\ \hline p43 & 5620 & 5623 & 5620 \\ \hline rbg323 & 1326 & 2755 & 2755 \\ \hline rbg358 & 1163 & 2755 & 2755 \\ \hline rbg403 & 2465 & 2755 & 2755 \\ \hline rbg443 & 2720 & 2821 & 2806 \\ \hline ry48p & 14,422 & 14,699 & 14,439 \\ \hline \end{tabular}
\end{table} 
Table 5: GVNS on asymmetric TSPs.
## 3 Feature extraction and dimension reduction
### Feature subset selection
#### 3.3.1 Genetic algorithm (GA)  
GA is a powerful optimization method that mimics natural evolution principles, consisting of three major operators: selection, crossover, and mutation [45, 46]. By employing an objective function constructed by selected variables, GA attempts to provide an optimal feature subset. In this optimization problem, a binary vector indicating which variables are selected into the feature subset is used as chromosome for the evolution process in GA and the objective function represented by classification or predication accuracy is used as a fitness function to evaluate chromosome quality.  
Figure 3 shows 10 biomarkers that were detected by GA to differentiate protein and virus from the output signals of a gas chromatography differential mobility spectrometry (GC/DMS) based sensor [47]. Using a PCA and neural network integrated classification model, these 10 biomarkers yielded 94% classification accuracy. The GA- driven feature subset selection approach was also applied for DMS based bacterial identification and Bacillus spore detection [48, 49] and MS based electric nose design [50]. Although GA can theoretically provide a global optimal solution, for a high dimensional optimization problem, GA is very likely to fall into local optima, which usually indicates a premature solution. It is therefore a key issue to define proper cross-over and mutation rates, although it can be difficult in some cases.
## 2 Protection System Architecture Based on Digital Twin Technology
Based on the panoramic and multidimensional data obtained from the secondary monitoring system, the digital twin model of the protection system can be constructed. The first includes common fault sets, classic domains and main characteristic quantities obtained by analyzing the historical database, and the three constitute the data resource center and are iteratively updated in real time. The second includes specific functional models, including a state evaluation model and a fault prediction model. The algorithmic process is integrated within the model to enable state evaluation and fault prediction of protection systems. The content of this paper will also focus on the twin model layer. 5. Application management layer. The application management layer is responsible for providing feedback on the state evaluation and fault prediction results of the digital twin layer, assisting operation and maintenance personnel to grasp the equipment situation and conduct preventive and targeted state maintenance to ensure the safe and stable operation of the protection system. The workflow of the digital twin model layer runs as follows. The intelligent sensing system and intelligent collection device in the secondary monitoring system can realize the comprehensive collection and live monitoring of equipment information, and transfer the operation data of the physical equipment layer of the protection system, including SOE operation records, self-test information, alarm information, etc., to the data-processing layer through the data transmission layer. The data-processing layer integrates the acquired data, organizes, mines, selects and analyzes them, and passes them to the twin model layer. The algorithm model within the model layer is used to conduct an evaluation of equipment status and fault prediction, and the results are passed to the application management. Figure 2: Master station architecture of secondary monitoring system. The staff will perform fault detection and maintenance of the equipment according to the prediction results, and then feedback the actual situation to the digital twin model. Through digital twin technology, the function of automatic optimization and real-time update is integrated into the operation management of protection system equipment, which can realize the functions of autonomous diagnosis and prediction, status evaluation, and planning decisions relating to the equipment. It enables the timely and reasonable allocation of resources to achieve preventive and targeted condition maintenance, thus realizing the effect of optimizing the overall operation of the protection system.
# GE-YOLO for Weed Detection in Rice Paddy Fields
## 3 Results and Discussions
### Comparative Analysis of the Improvement Strategies
The results of the above comparative experiments provide a comprehensive validation of the proposed improvements, demonstrating their effectiveness in enhancing the network's robustness and accuracy, particularly in the application of rice field weed detection tasks. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Strategy** & **Precision/\%** & **Recall/\%** & **mAP/\%** & **F1 Score/\%** & **GFLOPs** & **Params/M** & **Speed/FPS** \\ \hline AcMix & 92.1 & 87.9 & 92.1 & 90.0 & 17.8 & 8.12 & 55.9 \\ CBAM & 93.7 & 86.3 & 92.7 & 89.8 & 17.7 & 8.08 & 85.8 \\ ECA & 92.7 & 87.1 & 92.8 & 89.8 & 17.6 & 8.06 & 86.4 \\ GAM & 90.9 & 86.9 & 92.4 & 88.9 & 19.0 & 8.47 & 84.3 \\
**EMA** & **92.1** & **88.6** & **93.1** & **90.3** & **17.6** & **8.06** & **85.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Different Attention Mechanisms. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Loss function** & **Precision/\%** & **Recall/\%** & **mAP/\%** & **F1 Score/\%** & **GFLOPs** & **Params/M** & **Speed/FPS** \\ \hline EIoU & 92.2 & 84.3 & 90.8 & 88.1 & 17.7 & 8.06 & 92.2 \\ SIoU & 92.0 & 87.2 & 91.8 & 89.5 & 17.7 & 8.06 & 90.6 \\ WIoU & 59.4 & 75.7 & 69.4 & 66.6 & 17.7 & 8.06 & 84.5 \\ CIoU & 90.9 & 87.0 & 91.7 & 88.9 & 17.6 & 8.06 & 87.3 \\
**GIoU** & **92.1** & **88.6** & **93.1** & **90.3** & **17.6** & **8.06** & **85.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of Different Loss Functions.
## 4 Numerical Example: Transient Fin Problem
In addition, enhanced dissipation was considered to obtain the effects of out-of-plane dissipation. The convergence analysis for \(U_{V}\) is presented in Figure 4a, where the optimal was found at \(735\big{(}W\big{/}\big{(}\mathrm{m}^{2}\mathrm{K}\big{)}\big{)}/\mathrm{m}\) and is highlighted with the blue marker. The temperature and sensitivity information were extracted from the node located in the centerline of the fin's tip. For reference, the analytical solution at that point is presented as a solid black line in Figure 4b. The mesh was discretized along with the height, \(\delta[\mathrm{m}]\), using 10 equally spaced elements, and 1000 elements along the length, \(b\), and the time increment for the solver was set to \(\Delta t=1\) s. We used convergence analyses to set the values of discretization in space and time. We varied the number of elements in the domain by one order of magnitude up to \(10^{5}\) elements. A similar approach was followed for the time discretization up to a maximum of \(10^{4}\) time increments. Full details about the convergence analyses are presented in the Supplementary Material. Using the parameters in Table 1 (obtained from [28]), the analytical dimensionless temperature history, \((\theta)\), at the fin's tip (\(X=0\)) is presented in Figure 4b. In this figure, the analytical \(\theta\) history is presented as a solid black line, \(\theta_{SS}\) as a dashed red line, and the results using the Transient Thermal ZFEM UEL and the built-in Abaqus functions are overlaid using a yellow star and a blue square, respectively. This convention will be preserved for the rest of the results presented in this work. The sampling rate for the figures was every 14 time steps for visualization purposes. From the results presented in Figure 4b, it is noted that as the temperature approaches the steady-state (approximately at \(t=350\) s), and the simulations using the Transient Thermal ZFEM UEL and the built-in Abaqus functions match the temperature history of the analytical solution. Although the Transient Thermal ZFEM method for obtaining sensitivities can be applied to any input variable in the models, out of briefness, the next sections will focus on obtaining sensitivities for the transient behavior of a material property (\(k\)), a boundary condition (\(T_{\infty}\)), a geometric parameter (\(b\)), a temperature dependent-property (\(C_{P}(T)\)), and for the calculation of heating rates (\(\theta\theta/\partial t\)), which will cover all the perturbation cases described in Section 3. The results for the remaining parameters affecting the fin's thermal transient behavior are relegated to the Supplementary Material.
# A System Design Perspective for Business Growth in a Crowdsourced Data Labeling Practice
## 3 Payment Mechanism
### Position of the Payment Mechanism in the Working Process  
Figure 1 describes the general process of the labeling system, emphasizing the position of its credit function. Note that all the settings described are adjustable in the admin panel. Like any other system, the labeling practice starts by receiving the input data from customers and involving the crowd workers. The system must reward workers fairly and deliver accurately labeled data to customers. The design proposed involves several consecutive steps. Users enter the system through an authentication method and select their desirable datasets from among those available. They also determine their contribution level prior to starting, a procedure known as target setting. Users can then start labeling, i.e., submitting the appropriate answer to each item. Data are displayed until the number of labels assigned to each item reaches the pre-defined replication count.  
Figure 1: General process of a data labeling system.  
As users submit their answers, credit is updated by inputting the correct and/or incorrect responses to golden items into the proposed function. When a user completes labeling a target, he/she is allowed to collect the corresponding credit by transferring it to a virtual wallet. The last step requires aggregating the data labeled and reporting significant statistics to the system administrator. The payment mechanism is at the core of the labeling system and has a two-way communication relation with the preceding and succeeding steps. That is, a breakdown in communication would result from a failure of the administrator to embed the practical features of the credit function into the body of the labeling system. Moreover, the corresponding function or payment mechanism is crucial for feeding the labeling aggregation and reporting results with the required information.
# Research on Real-Time Control Strategy for HVAC Systems in University Libraries
## 3 Experimental Cases
### Device Model and Objective Function
#### 3.2.3 System Model and Boundary Conditions  
The energy consumption of the entire HVAC system is modeled using the above model of pumps and chillers with the objective function.  
\[\begin{array}{l}P_{xyz}=P_{system}\\ =\sum p_{\rm chiller}^{1}+\sum p_{\rm chiller}^{2}\rm{temperature}+\sum p_{\rm cooling }^{3}\rm{waterpump}\\ =F(Q_{L},t,hu,G_{w,\rho}T_{w,\rho,L})\end{array} \tag{10}\]  
In this function, the cooling load \(Q_{L}\), temperature t, and humidity hu are the input parameters that agree with the actual situation, whereas \(G_{w,0}\) and \(T_{w,v,L}\) are the parameters to be adjusted. When these parameters are determined, other required physical quantities can be obtained according to the conservation of mass and energy. The \(P_{\rm chiller}\) can be calculated using Equations (1) and (6), while the chilled water pump power and cooling water pump power can be calculated using Equation (8). The objective of the optimization algorithm is to determine the appropriate \(G_{w,0}\) and \(T_{w,v,L}\) to minimize the energy consumption of the system.  
The energy consumption of the HVAC system was calculated as shown in Equation (10). After sensing the parameters QL, t, and hu, the current chilled water inlet temperature and set chilled water output temperature were utilized to calculate the chilled water flow rate, power consumption, and evaporative temperature of the chiller t\({}_{\text{e}}\). The chilled water flow rate, power consumption, and cooling tower energy consumption were calculated based on the current chilled water flow rate. The chiller power consumption was obtained by iteratively calculating the evaporation temperature based on the circulating chilled water flow rate and determining the condensation temperature. Finally, the power consumption of the entire system was obtained using the current parameter settings.  
In the algorithmic control module of an HVAC system, the equations of conservation of mass and conservation of energy are indispensable tools in the modeling and optimization process. Not only do these equations help to ensure that the physical processes of the system conform to the basic laws of physics as constraints for the optimization algorithms to ensure that the flow rate of the system remains balanced during operation, but they also allow for the calculation of cooling and chilled water flow rates based on known flow rates or pressure differentials and the detection of leaks or other anomalies in the system through the monitoring of flow rate variations, amongst other uses.
## 2 Materials and Methods
### Numerical Model
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Parameter** & **Value** \\ \hline \multirow{3}{*}{Fluid properties} & Oil–water ratio & 1:9 (volumetric) \\  & Oil density & 850 kg/m\({}^{3}\) \\  & Water density & 998 kg/m\({}^{3}\) \\  & Dynamic viscosity & 0.001 Pa\(\cdot\)s \\ \hline \multirow{3}{*}{Boundary conditions} & Inlet velocity & 50\(-\)150 m\({}^{3}\)/d \\  & Rotational speed & 3500 rpm \\  & Boundary conditions & Velocity inlet, pressure outlet \\ \hline \multirow{3}{*}{Numerical settings} & Turbulence model & Standard k-\(\omega\) \\  & Near-wall treatment & Enhanced Wall Function \\ \cline{1-1}  & Convergence & Residuals \(<10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key parameters for CFD simulations. Figure 5: Shear stress distribution of oil-water two-phase flow on the ESP impeller surface. Figure 6: Dimple-type non-smooth structures: (**a**) on leading edge of suction surface; (**b**) on trailing edge of pressure surface; and (**c**) parametric configuration of non-smooth surface structures. The pump head \(H\) is defined as follows:  
\[H=\frac{p_{2}-p_{1}}{\rho g}+\frac{v_{2}^{2}-v_{1}^{2}}{2g}+(z_{2}-z_{1}) \tag{7}\]  
where \(p_{1}\) and \(p_{2}\) represent inlet and outlet pressures, \(v_{1}\) and \(v_{2}\) denote fluid velocities at corresponding cross-sections, \(z_{1}\) and \(z_{2}\) indicate pressure sensor elevations. Pump efficiency \(\eta\) is expressed as follows:  
\[\eta=\frac{P_{e}}{P}\times 100\% \tag{8}\]  
where the effective power \(P_{e}\) and shaft power \(P\) are calculated as follows:  
\[P_{e}=\frac{\gamma\mathrm{Q}H}{1000} \tag{9}\]  
\[P=\alpha\frac{Tn}{9550} \tag{10}\]  
where \(\gamma\) is the fluid's specific weight, \(Q\) is flow rate, \(T\) is torque, \(n\) is rotational speed, and \(\alpha\) represents a correction factor [27]. Head gain (\(\Delta H\)) and efficiency gain (\(\Delta\eta\)) are defined as follows:  
\[\Delta H=\frac{H_{2}-H_{1}}{H_{1}}\times 100\% \tag{11}\]  
\[\Delta\eta=\frac{\eta_{2}-\eta_{1}}{\eta_{1}}\times 100\% \tag{12}\]  
\(H_{1}\), \(\eta_{1}\): head and efficiency of the original model (smooth surface); \(H_{2}\), \(\eta_{2}\): head and efficiency of the modified impeller (non-smooth surface).
## 5 Defense Frameworks in FL  
Given the variety of attacks in FL, it is imperative to deploy a range of defense frameworks tailored to the specific nature of each attack, considering factors such as device configurations, FL architecture, and available resources. Each type of attack, including data, model, and privacy, exploits different vulnerabilities within the FL ecosystem, necessitatingdistinct defensive strategies [68]. For instance, robust data validation and anomaly detection systems can mitigate data poisoning attacks [69], while secure aggregation protocols and byzantine fault tolerance mechanisms are more effective against model poisoning [70]. Privacy-preserving techniques, such as differential privacy and homomorphic encryption, are essential to protect against inference and membership inference attacks. Due to the diverse nature of these threats, a one-size-fits-all defense mechanism is impractical. Instead, a layered defense approach, incorporating multiple frameworks designed to address specific attack vectors, ensures comprehensive protection and enhances the overall security and resilience of FL systems [71].  
To counter the emerging threats FL, researchers have proposed a variety of defensive strategies [72; 73; 74]. These defense mechanisms address adversarial attacks across distinct phases of the learning process: pre-aggregation, in-aggregation, and post-aggregation. Pre-aggregation defenses [72; 73; 75] focus on early identification and mitigation of malicious updates before they impact the global model. In-aggregation defense techniques [64; 74; 76; 77; 78] employ robust aggregation operators to mitigate adversarial effects during the global model update phase. Conversely, post-aggregation defense strategies [79; 80; 81] concentrate on repairing adversarial models after completing the FL training process, ensuring the final model's integrity. These phase-specific defense mechanisms are crucial for enhancing the security and reliability of FL systems against backdoor attacks. The two main categories of defenses based on attack types are data and model defense, and privacy defense. These defense frameworks can be further categorized based on their working principles, as depicted in Figure 5.
## 2 FANET Architecture and Routing Protocol  
We briefly review the FANET communication and application architecture for discussion and discuss in detail the FANET routing protocols and their development in recent years.
# Vegetation Growth Response and Trends after Water Deficit Exposure in the Loess Plateau, China
## 2 Materials and Methods
### Data Sources  
#### 2.2.1 Meteorological Data  
The monthly precipitation dataset and the monthly mean temperature dataset, with 1 km resolution, were provided by the National Earth System Science Data Center, National Science and Technology Infrastructure of China ([[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn)), accessed on 11 May 2022). The precipitation and temperature datasets were generated by the delta spatial downscaling scheme in the Chinese region, based on the global 0.5\({}^{\circ}\) climate data released by CRU and the global high-resolution climate data released by WorldClim. The accuracy and reliability of the datasets were verified by comparing them with data from 496 independent meteorological observation stations, and the validation results were credible [34]. Monthly temperature and precipitation data from 1999 to 2020 were utilized to calculate SPEI to examine the meteorological changes and assess the response of vegetation  
Figure 1: Background information on the study area: **(a)** location; **(b)** vegetation cover type data; and **(c)** elevation.  
to varying durations of water deficit. To facilitate the calculation, the spatial resolution of all data was uniformly resampled to 0.1\({}^{\circ}\) for this study.
# Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis
## 2 Wind Turbine Benchmark
### WT Benchmark Model  
The WT simulator considered in this work was presented in [3;20] and motivated by an international competition. Despite its simple structure, it is able to describe quite accurately the actual behaviour of a three-blade horizontal-axis WT that is working at variable-speed and controlled by means of the pitch angle of its blades. The plant includes several interconnected subsystems, namely the wind process, the WT system, the controllers, and the condition monitoring module. The WT block includes the WT aerodynamics, the drive-train, the electric generator/converter, the sensor and the actuator systems, which are not detailed here. The overall scheme is sketched in Figure 1, which represents the FDItarget of this work. The diagram of Figure 1 includes also the switching control strategy driving the WT system in partial and full load working conditions [20]. These operating regions will be exploited for the generation of the estimation and validation data sequences, as remarked in Section 4.  
The nominal operating conditions of the WT are selected to satisfy different demands below and above a certain wind speed. The control task is thus split into the partial and full load controllers, as shown in Figure 1. The condition monitoring system depicted in Figure 1 determines the actual working point of the WT plant and drives the controlled switches accordingly.  
The measurements that are available from the WT benchmark for FDI purpose come directly from several sensors or, in one case, they are obtained via estimation. In particular, for each of the three blades, a redundant couple of sensors measures the current pitch angle \(\beta_{i,m\,j}\). Then, a couple of sensors measures the speed of the rotor \(\omega_{r,m\,j}\) and another one the speed of the generator \(\omega_{g,m\,j}\). A single sensor is available for the wind speed at hub height \(v_{w,m}\), and another one for the generator torque \(\tau_{g,m}\). The wind measurements are estimated exploiting the hub anemometer. Table 1 reports a summary of the measured variables. The model of the measurements consists of the sum of the actual value with a white Gaussian noise process [20].  
\begin{table}
\begin{tabular}{c c c c c c} \hline Variable & \(\beta_{1,m1}\) & \(\beta_{1,m2}\) & \(\beta_{2,m1}\) & \(\beta_{2,m2}\) & \(\beta_{3,m1}\) & \(\beta_{3,m2}\) \\ \hline Pitch \# & \(1\) & \(1\) & \(2\) & \(2\) & \(3\) & \(3\) \\ Sensor \# & \(1\) & \(2\) & \(1\) & \(2\) & \(1\) & \(2\) \\ \hline Variable & \(\omega_{r,m1}\) & \(\omega_{r,m2}\) & \(\omega_{g,m1}\) & \(\omega_{g,m2}\) & \\ \hline Speed & Rotor & Rotor & Generator & Generator & \\ Encoder \# & \(1\) & \(2\) & \(1\) & \(2\) & \\ \hline Variable & \(\tau_{g,m}\) & \(P_{g,m}\) & \(v_{w,m}\) & \(\tau_{r,m}\) & \\ \hline Measure & Generator torque & Generated power & Wind speed & Rotor torque & Estimated \\ Model & Generator & Generator & Anemometer & & \\ \hline \end{tabular}
\end{table}
Table 1: Signals acquired from the WT benchmark.  
Figure 1: The WT benchmark and its main functional subsystems.