esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Methods Comparison of Shock-Induced Separation on Transonic Axisymmetric Hump
## 2 Methodology  
### Two-Equation Shear-Stress Transport (SST) Turbulence Model  
The two-equation k-\(\omega\) SST model is an eddy viscosity model formulated to close the Reynolds-Averaging Navier-Stokes equation. Essentially, it combines the \(k-\varepsilon\) model [24] and the Wilcox \(k-\omega\) model [25; 26]. Near the wall zone, the transferred \(k-\varepsilon\) model is combined with the Wilcox \(k-\omega\) model. At the edge of the boundary layer and free shear layer, the transferred \(k-\varepsilon\) model is applied, and the standard \(k-\varepsilon\) model is used in the fully turbulent region far from the wall. The \(k-\omega\) model contains \(k\) equation and \(\omega\) equation as follows:  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ujk)}{\partial x_{j}} = P-\beta^{*}\rho\omega k+\frac{\partial}{\partial x_{j}}((\mu+ \sigma_{k}\mu_{t})\frac{\partial k}{\partial x_{j}}) \tag{1}\]  
\[\begin{array}{rl}\frac{\partial(\rho\omega)}{\partial t}+\frac{\partial( \rho uj\omega)}{\partial x_{j}}&=\frac{\gamma}{\
u_{t}}P-\beta\rho\omega^{2}+ \frac{\partial}{\partial x_{j}}((\mu+\sigma_{\omega}\mu_{t})\frac{\partial \omega}{\partial x_{j}})\\ &\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad +2(1-F_{1})\frac{\rho(\sigma_{k}\omega)}{\omega^{\prime}}\frac{\partial k}{ \partial x_{j}}\frac{\partial\omega}{\partial x_{j}}\end{array} \tag{2}\]  
where \(k\) means turbulent kinetic energy and \(\omega\) means the rate of dissipation of turbulence per unit energy. \(\beta,\ \beta^{*},\gamma,\sigma_{k}\)_and_\(\sigma_{\omega}\) are the closure coefficients equal to 0.075, 0.09, 0.556, 0.85, and 0.5, respectively. The \(\mu\) means the molecular viscosity, \(\
u_{t}\) and \(\mu_{t}\) mean the turbulent viscosity, and \(\
u_{t}=\mu_{t}/\rho\). \(F_{1}\) relates to the mixing function of \(k-\varepsilon\) and \(k-\omega\) model. The form of the \(k\) equation will be changed in the following different methods.
## 2 Materials and Methods
### Chromatographic Conditions  
The extract was analyzed with a Diode Array Detector (DAD), Fluorescence detector, and colon (Poroshell 120 EC-C18 4.6 \(\times\) 150 mm, 4 \(\upmu\)m). MelQx, 4,8 DiMeIQx, and IQ compounds were detected by DAD detector (Agilent Serial No: DEAC612736). The IQ was studied at 254 nm; MeIQx and 4,8-DiMelQx were measured at 263 nm. The mixture of acetic acid/methanol/acetonitrile/water/ (2/8/14/76 \(v/v/v\)) at pH 5.0 (adjusted with ammonium hydroxide 25%) was used as mobile phase A, while acetonitrile (100%) was used as mobile phase B. The flow rate was 0.3 mL/min for the first 35 min and was then changed to 1 mL/min. The flow rate was again 0.3 mL/min at the 49 th minute and was constant until the end of the analysis. The injection volume was 20 \(\upmu\)L. The run time analysis was 55 min. A gradient program (0-20 min 100% A, 20-34 min 70% A, 34-49 min 0%A, and 49-55 min 100% A) was used. Harman, norharman, and PhIP compounds were detected by fluorescence detectors at 340 nm and 370 nm as excitation and emission wavelengths, respectively. A total of 0.5 M ammonium acetate was used as mobile phase A, while methanol (100%) was used as mobile phase B at a flow rate of 0.5 mL/min. The injection volume was 15 \(\upmu\)L. The run time analysis was 30 min. A gradient program (0-10 min 90% A, 10-15 min 75% A, 15-20 min 50% A, 20-22 min 0% A, and 25-30 min 90%A) was used. The retention time of unknown peaks in the sample chromatogram was determined by comparison with reference standards used to identify HCAs. The number of HCAs were calculated as ng/g cooked coated chicken liver samples.  
LOD, LOQ, and Recovery of HCAs  
The analysis methods for the HCAs were validated in terms of limits of detection (LOD), limit of quantification (LOQ), correlation coefficients (R\({}^{2}\)), and recovery (%). The LOD and LOQ were calculated based on signal to noise ratios of 3 and 10, respectively. The quantification of the HCAs was based on external calibration. The calibration curves were established from the known concentrations. Recovery rates for the different HCAs in the samples were determined by the standard addition method [35]. The regression coefficients (R\({}^{2}\)) for the standard curves were greater than 0.99 for all the HCAs identified in this study.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 2 Important Applications of UAVs in Smart Cities
### Construction and Infrastructure  
Drones are used for surveying land, inspecting structures, and monitoring construction progress. They can provide high-resolution images and videos that can help in planning, monitoring, and inspecting construction sites [20].
## 2 System Model
### Reconfigurable Intelligent Surface System Model
In order to measure the ASE, we introduce the fractional frequency reuse (FFR) scheme into the system model where a unity frequency reuse factor (FRF = 1) is assigned at the cell center while the cell edges are assigned FRF of three (FRF = 3). Let the total available bandwidth be partitioned into two disjoint sets of non-overlapping frequency bands. At the cell edges, the sub-band frequency is divided among them in such that the frequency allocated is different from their adjacent cells. We assume that all of the RISs at the cell edge serve the mobile user \(U\) in the edge region using the maximized strategy as analyzed in the previous section. Then, the average area spectral efficiency can be defined as the maximum average data rates per unit bandwidth per unit area. Based on (14), we arrived at the following expression for the area spectral efficiency (ASE) for RIS at the cell edge in bits/s/Hz/km\({}^{2}\) as [15]:  
\[ASE_{RIS}=q\frac{E[C_{RIS}]}{\pi R^{2}(FRF)}. \tag{16}\]
## 4 Simulation Results  
To validate the effectiveness of the proposed algorithm, a microgrid is simulated using MATLAB. The output power of its DG units and grid is set by the proposed EMS. The simulated microgrid consists of PV, WT, DEG, FC, battery storage and a connection to the utility grid. This benchmark has been used by previous research [31] and has been chosen to be simulated in this paper in order to be able to compare obtained results with previous research results available in literature. Table 1 shows the minimum and maximum output power of each element of the microgrid as well as coefficients of bid functions, where the negative sign indicates direction of power flow. Table 2 presents sample forecast values for the load demand and DG units' powers along with market price. Two case studies are simulated to evaluate the effectiveness of the proposed HHO-based EMS on different operating conditions. Cost is expressed in units of euro cents (\(\mathbf{\xi}\) ct).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ID** & **DG** & **Minimum Power (kW)** & **Maximum Power (kW)** & \(\mathbf{a_{i}}\) (\(\mathbf{\xi}\)ct/kw \(\mathbf{h}^{2}\))** & \(\mathbf{b_{i}}\) (\(\mathbf{\xi}\)ct/kw \(\mathbf{h}\)) & \(\mathbf{c_{i}}\) (\(\mathbf{\xi}\)ct/h)** \\ \hline
1 & PV & 0 & 25 & 0 & 2.584 & 0 \\
2 & WT & 0 & 15 & 0 & 1.073 & 0 \\
3 & MT & 6 & 30 & 0 & 0.457 & 0 \\
4 & FC & 3 & 30 & 0 & 0.294 & 0 \\
5 & Battery & \(-\)30 & 30 & 0 & 0.38 & 0 \\
6 & Utility & \(-\)30 & 30 & 0 & - & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Power limits and coefficients of bid functions of the installed distributed generation (DG) units.  
Figure 4: The flowchart of the proposed EMS.
## 2 Related Work
### Smart Farming  
Smart farming is described as a farming system in which innovative and cutting-edge technologies are used with conventional farming practices to increase farmland production quantity and quality while dramatically reducing manufacturing costs [16]. Smart farming and traditional farming are completely different from one another. Traditional farming practices include employing outdated equipment for labor and cultivating seasonal crops without first determining market demands and prices, or taking into account weather data from the weather service, among other things. Technological advances are used in smart farming, including smart devices, IoT sensors, cloud/fog computing, UAV data collection, and periodic assessments of various aspects. This makes farming simple and inexpensive with minimum labor costs. and results in improved crop yields and increased productivity [17]. The primary emphasis is on the integrated and coordinated implementation of new technologies into smart farms while offering sophisticated agriculture management that draws on collective expertise and judgment. Some noteworthy attempts include the Soil Scout [18] and Thoreau [19] projects, which monitor soil properties and farming conditions [20] using wireless sensor nodes.  
While traditional farming devices are designed to have a strong connection and low power utilization, they are inadequate for carrying out sophisticated operations. IoT devices, in contrast, are linked and have the potential to offload operations to the cloud or spread them across several devices. The advantages of multiple farms linked with sensors and actuators connected across an IoT access point that deliver smart agricultural systems for end users are explored in [21]. The IoT devices are also linked to a server that keeps track of all the interconnected farms. IoT gateway-based surveillance software in [22] evaluates the leaf area index. Since the Internet of Things-based data analytics operates with a range of sensors, solutions can only accommodate homogeneous sensors.  
Despite these efforts, a significant obstacle to widespread farmer adoption of such networked solutions remains, which is the availability of configurable hardware that can be programmed for customized data collection. A system employing open hardware to allow the creation of a smart farming framework is presented in [23] to address this difficulty. Furthermore, a proposed methodology in [24] introduces a smartphone-based smart agricultural system. Users of the system can operate the installed sensor modules or gather and evaluate agricultural data from the environment. The overlying infrastructure is adaptable and enables dispersed service deployment. These frameworks allow for the creation of reproducible IoT-based farming applications.
# Positioning Methods and the Use of Location and Activity Data in Forests
## 2 Types of Positioning Technologies
### Ultra Wideband and UHF/VHF Radio Telemetry
Positioning approaches for terrestrial wildlife usually involve GNSS devices that transmit information to researchers using commercially-available or private satellite networks, or cellular tower networks, and are thus functionally similar to safety devices such as Spot(tm) and Garmin inReach(tm) that also use two-way satellite data communication. For aquatic wildlife, GNSS loggers have also greatly improved researchers' monitoring abilities, as has the advent of acoustic arrays that can monitor animals that never come to the surface [96]. Coupling these new location technologies with bio-loggers, which can measure animal physiological states (e.g., heart rate, caloric expenditure, and environmental conditions including sunlight, salinity, temperature, and depth), and even with drones equipped with onboard cameras and acoustic recorders, is leveraging these new technologies even further [98; 99; 100]. The resulting fine-grained and real-time data on animal locations and behaviors can be combined with monitoring systems to improve human and wildlife safety and to enhance conservation of rare species (e.g., proximity, immobility or death, geofencing, and movement or behavior classification [101]). One consideration that is important for active (i.e., UHF) radio tags and miniaturized GNSS transponders for wildlife, which may be less critical for operational forestry, is the need to conserve battery power. Receipt and transmission of coordinates from wildlife location devices must generally be limited to lower coordinate transmission intervals because of the potential for rapid loss of battery power, which is a lower concern for forest workers who can charge devices each evening, or onboard equipment applications with continuous power. Figure 5: UWB positioning using TOA (time of arrival) trilateration. A tag (worm by the deer) emits UWB pulses which are received at each of the three anchor nodes. Based on a known signal speed and the propagation delay (signal travel time, \(t\)), each receiver can calculate its distance, \(d\), from the tag. The area where the three distances intersect (shown with a yellow outline) is the estimated position of the tag [91; 93].
# Effect of Mix Proportion Parameters on Behaviors of Basalt Fiber RPC Based on Box-Behnken Model  
[PERSON] [PERSON] [PERSON] and [PERSON]  
1 College of Transportation, Jilin University, Changchun 130025, China; [EMAIL_ADDRESS] (H.L.); [EMAIL_ADDRESS] (S.L.); [EMAIL_ADDRESS] (S.W.); [EMAIL_ADDRESS] (X.G.) 12223  
decrease and high cost. With these concerns in mind, researchers began to replace steel fibers with natural fibers, which have good performance, a low price and are lightweight.  
Basalt fiber is a kind of environment-friendly material made of natural basalt ore [11]. In recent years, basalt fiber has been widely used in the reinforcement of concrete materials due to its excellent performance of mechanical characteristics, corrosion resistance, high temperature resistance and electrical insulation [12; 13]. It has been confirmed that the enhancing effect of 12 kg/m\({}^{3}\) basalt fiber on the strength of concrete is similar to that of 40 kg/m\({}^{3}\) steel fiber under the action of bending and impact load [14]. For the 28-day compressive strength of ordinary concrete, the optimal content of 12 mm long basalt fiber is 4 kg/m\({}^{3}\) and the optimal content of 36 mm long is 8 kg/m\({}^{3}\)[15]. Since basalt fiber performs well in the strength enhancement of ordinary concrete, researchers then apply it to UHPC. [PERSON] et al. [16] reported that when silica fume and kaolin were used instead of 10% cement in UHPC, the highest compressive strength of 84.08 MPa could be obtained at a volume content of 2% basalt fiber. [PERSON] et al. [17] investigated that the compressive and flexural strength of UHPC could be increased by 14% and 22.8% respectively under the synergetic effect of basalt fiber and polypropylene fiber. However, there are seldom research on adding basalt fiber into RPC as reinforcing material, and most studies take fiber content as a single factor variable. The systematic analysis of the interaction between RPC mix parameters and basalt fiber has not been carried out. Realizing this issue, the goal of this work is to characterize the influence of multiple mix factors and basalt fiber interaction on RPC properties, and provide reference for the mix design methods of basalt fiber reactive powder concrete (BFRPC). In this study, the relationship between four process variables (sand binder ratio, water binder ratio, basalt fiber volume and silica fume to cement ratio) along with workability and mechanical properties of BFRPC were modeled using the response surface method (RSM) [18]. Based on the established models, the influence degree and rule of process variables on material fluidity, flexural strength and compressive strength were determined.
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 3 Problem Statement  
There are some of the issues in the existing works of Handover management in 4G networks. Handover management technique [13] is used to reduce handover delay and call dropping. Here, the prediction component of the MFNN (Multi-layer Feed Forward Network) is used to record the mobility pattern of the user. The data cleaning component of the PISMA (Prefix Tree Sequence Mining Algorithm) filters only relevant patterns from the predictive model. Handover may fail, though, when prediction of the target base station is not in the neighbor of the source base station. If the predicted cell is not suitable for the user requirements, then it will degrade the quality of service (QoS).  
In [17], the primary downside of expanding the lifetime of the Mobile Terminal inside the WLAN is expanding the packet delay coming about because of channel condition corruption. For the decision creation of a network, the fuzzy based decision making is clever; however, increasingly, a number of fuzzy standard sets ought to be framed. In QoS based VHO decision algorithm [10], the frequent vertical handover can cause wastage of network resources. Additionally, this is suitable only for soft vertical handover.  
The Vehicular Handover Decision (VHD) algorithm [30] increases the dropping rate to choose the good network from the present network. The drawback of the HNE (Handover Necessity Estimation) [31] is that the throughput metrics for the mobility management technique were not considered. In [32], if the speed will increase, the number of packets dropped will also increase. A new location in which the mobile does not receive packets from the old base station is the settling time in which packet loss occurs.
# An Enhanced Multi-Constraint Optimization Algorithm for Efficient Network Topology Generation
## 4 Algorithm Design
### Delete Link  
Following the establishment of the initial links in Section 3.1 and the assignment of weights to the links in Section 3.2, a process to delete poor-performing potential links for satellite nodes with more potential links than the node degree is introduced. First, a link with the smallest weight is removed from these nodes. This operation is executed iteratively until all satellite nodes have a number of potential links less than the node degree, ultimately resulting in a satellite network topology. The detailed procedure of the link deletion algorithm is demonstrated in Algorithm 3.  
```
Input: Edge set matrix \(E^{t}\) of inter-satellite links
1. Output:\(E^{t}\)
2 Initialize variables \(i\) and \(y\);
3 for\(i=1\)to\(n\)do
4 Initialize node \(V_{i}\);
5 if The initial number of links for node \(V_{i}\) > node connectivity degree \(d\)then
6 Sort all initial links of node \(i\) according to the link weights;
7 Delete the links with the smallest weights;
8 Set \(e_{ij}^{t}=0\);
9 until\(v_{i}=d_{i}\);
10
11 return\(E^{t}\);
```  
**Algorithm 3**Link deletion algorithm.  
Upon making decisions on the deletion of links through the link deletion algorithm for the initialized potential links, an optical communication constellation network topology with the same number of nodes as the satellite nodes in the constellation network is generated, where the number of inter-satellite links for each satellite node does not exceed the number of optical transceivers. The complete procedure of the MCOTG algorithm is illustrated in Figure 3.
# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 6 Conclusions and Future Work  
This paper addresses the challenges of cross-domain few-shot fault diagnosis, which include limited samples, complex working conditions, and diverse fault types. To enhance the model's generalization ability, we propose a federated few-shot fault-diagnosis method that leverages convolutional denoising auto-encoder and feature-space metric learning. The approach involves training the model using fault data from various source domains while ensuring data privacy through federated parameter aggregation. By incorporating convolutional denoising auto-encoder and feature-space metric learning, the model's generalization across different working conditions and fault types is significantly improved, leading to enhanced diagnostic accuracy. The effectiveness of the proposed method is demonstrated through experimental results on the CWRU bearing and gearbox datasets.  
In the future, our research will encompass several key objectives. First, we will explore the applicability of our FedCDAE-MN to various scenarios within the manufacturing process, such as process performance prediction, product defect detection, and more. Second, our efforts will be directed toward enhancing the interpretability of our model. To achieve this, we will explore the knowledge-data dual-driven method for fault diagnosis. Lastly, we intend to investigate the issues of federated few-shot learning-based fault  
Figure 9: Average accuracy under different SNRs.  
diagnosis across multiple domains with heterogeneous resources, seeking the optimal trade-off between model utility and resource consumption.  
Conceptualization, Y.L. and P.Z.; methodology, Y.L. and Y.W.; software, Y.L.; validation, Y.L., P.Z. and Y.W.; formal analysis, Y.L. and P.Z.; investigation, Y.L.; resources, Y.L.; data curation, Y.L.; writing--original draft preparation, Y.L. and Y.W.; writing--review and editing, Y.L. and P.Z.; visualization, Y.L.; supervision, P.Z.; project administration, P.Z.; funding acquisition, P.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by NSFC Grant No. 62172329.  
Not applicable.  
Data Availability Statement: Data used to support this study are available from the authors upon request.  
The authors declare no conflict of interest.
# Selecting Non-Line of Sight Critical Scenarios for Connected Autonomous Vehicle Testing
###### Abstract  
The on-board sensors of connected autonomous vehicles (CAVs) are limited by their range and inability to see around corners or blind spots, otherwise known as non-line of sight scenarios (NLOS). These scenarios have the potential to be fatal (critical scenarios) as the sensors may detect an obstacle much later than the amount of time needed for the car to react. In such cases, mechanisms such as vehicular communication are required to extend the visibility range of the CAV. Despite there being a substantial body of work on the development of navigational and communication algorithms for such scenarios, there is no standard method for generating and selecting critical NLOS scenarios for testing these algorithms in a scenario-based simulation environment. This paper puts forward a novel method utilising a genetic algorithm for the selection of critical NLOS scenarios from the set of all possible NLOS scenarios in a particular road environment. The need to select critical scenarios is pertinent as the number of all possible driving scenarios generated is large and testing them against each other is time consuming, unnecessary and expensive. The selected critical scenarios are then validated for criticality by using a series of MATLAB based simulations.  
connected autonomous vehicle; non line of sight; critical scenario; evolutionary algorithm; concrete scenario +
Footnote †: journal: Software