esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Establishment of Forecasting Modules for Frequency Nadir and Frequency Support Capability  
### Frequency Nadir Forecasting Module  
In the proposed preventive control strategy, the frequency nadir forecasting module is trained by back-propagation artificial neural network (BP-ANN), which capable of accurately predict the system frequency nadir under an \(N\)-1 contingency [1,2]. The frequency nadir forecasting module using BP-ANN is illustrated in Figure 9. The input data include the power output of diesel generators (\(P_{DG}\) (MW)), power output of wind turbines (\(P_{wind}\) (MW)), power output of photovoltaics (\(P_{PV}\) (MW)), system load (\(P_{Load}\) (MW)), computed system moment of inertia (\(\mathbf{H}\)) and computed spinning reserve (\(\Delta\mathbf{SR}\)) and the power loss owing to an \(N\)-1 tripping event (\(\Delta\mathbf{P}\)). The output data of the module are the frequency nadirs without any frequency-support control.
## 2 Data and Methods
### Modeling the Impact of Soil Moisture Stress on GPP  
In this study, the temperature (TA_F), VPD (VPD_F), CO\({}_{2}\) (CO\({}_{2}\)_F_MDS), and incident shortwave radiation (SW_IN_F) data, as well as MOD09A1 reflectance data and GLASS LAI data, were used to calculate GPP. The optical parameters, such as LUE of the TL-LUE model and VPM were recalibrated based on the FLUXNET GPP with the least squares method to achieve better simulation results. The GLASS LAI and MODIS reflectivity data are first stitched and projected, and then the values at different sites are extracted.  
We used _f(SM)_, a sigmoid-type function with permanent wilting point and field water holding capacity as subdivision points, as the model improvement equation for integrating SM. The _f(SM)_ expression is as follows:  
\[f(SM)=\begin{cases}0&sm<a\\ \frac{1}{1+e^{-sm}}&a\leq sm<b\\ 1&sm\geq b\end{cases} \tag{30}\]  
\(sm\) is soil moisture, \(a\) denotes the permanent wilting point, and \(b\) stands for field water holding capacity. For model improvement, a comparative analysis of three scenarios is considered in this study.  
Scheme 1: Replacing the moisture function in the original model using _f(SM)_. The improved TL-LUE and VPM expressions were multiplied directly by the _SM_ correlation function _f(SM)_, as follows:  
\[GPP=(\epsilon_{su}\times APAR_{su}+\epsilon_{sh}\times APAR_{sh})\times f(T) \times f(SM) \tag{31}\]  
\[GPP=\epsilon_{\text{max}}\times ft\times f(SM)\times P\times FAPAR_{NPV}\times PAR \tag{32}\]  
Scheme 2: In a similar manner to other environmental stresses, the improved TL-LUE and VPM expressions were multiplied directly by the _SM_ correlation function _f(SM)_, as follows:  
\[GPP=(\epsilon_{su}\times APAR_{su}+\epsilon_{sh}\times APAR_{sh})\times f(VPD )\times f(T)\times f(SM) \tag{33}\]  
\[GPP=\epsilon_{max}\times ft\times f(SM)\times P\times FAPAR_{NPV}\times PAR \tag{34}\]  
Scheme 3: The minimum value is taken among the existing moisture data and the newly constructed _f(SM)_.  
\[GPP=(\epsilon_{su}\times APAR_{su}+\epsilon_{sh}\times APAR_{sh})\times f(T) \times fw^{\prime} \tag{35}\]  
\[GPP=\epsilon_{\text{max}}\times ft\times fw^{\prime}\times P\times FAPAR_{NPV }\times PAR \tag{36}\]  
\[fw^{\prime}=\begin{cases}fw&fw\leq fsm\\ fsm&fw>fsm\end{cases} \tag{37}\]
# Improving Prostate Image Segmentation Based on Equilibrium Optimizer and Cross-Entropy
## 4 Minimum Cross-Entropy by EO for MRIs
### Computational Complexity  
The proposed MCE-EO uses two algorithms with different properties. First, the calculation of the minimum cross-entropy (MCE) for \(t\) thresholds is \(O(L^{t+1})\)[12]. The second algorithm is the equilibrium optimizer (EO) with a reported polynomial complexity of \(O(idn+cin)\), where \(c\) is the cost of the objective function (in this case MCE), \(i\) is the number of iterations, \(d\) indicates the dimensions, and \(n\) indicates the number of solutions at each generation [25].
# A Modeling Method of Agents and SOA in Advanced Avionics System Based on AADL
## 2 Background
### Research and Modeling Methods for SOA  
Up to now, there is still not a definition of SOA that is universally accepted, but some are popular in the industry. According to [PERSON], SOA represents an open, agile, extensible, federated, and composable architecture, consisting of self-contained, high-quality, inter-operable, discoverable, and potentially reusable services [30]. In our previous work, the concept of service-oriented advanced avionics architecture was put forward, and service components and management framework of SOA in avionics systems were defined [31]. SOA working logic in avionics is shown in Figure 1. The bottom two layers are the operating system and hardware of the avionics system, which provide hardware resources and basic system functions for the whole system. The next layer is the service layer, which is oriented to user application requirements and encapsulates business logic into services with explicit functions, which are provided to applications and other services for invocation. Above the service layer is the application layer, which is the business logic entity that invokes various services for different task requirements. Between the application layer and the service layer is the invocation relationship from the application to the service and between the services. These invocation relationships represent how the application invokes the service to achieve the task requirements. Above the application layer is the task layer. The tasks accomplish the system goals through the invocation of applications, the invocation of applications to services, and the management and operation of system resources by services.  
In the current SOA research and project practice, some methods, such as object-oriented analysis and design method (OOAD), and business process modeling (BPM) methodology have been applied to SOA modeling. In SOA modeling, service choreography and other contents should be paid attention to on the basis of software architecture, and many design principles in OOAD are also carried out by SOA inheritance [32], but OOAD applied in SOA modeling mainly abstracts the system from the class and object instance levels, and lacks the description of the overall business architecture, when confined in a single application. In the process of modeling SOA with BPM, the core is the management of the process, and the essence is to take the process as new abstract data [33]. [PERSON] et al., proposed a process-driven modeling approach that focuses on SOA modeling in the context of business processes [34]. In this method, although the process management of the work unit is realized, the description of the business implementation and architecture in the system is missing.
# Corn Plant In-Row Distance Analysis Based on Unmanned Aerial Vehicle Imagery and Row-Unit Dynamics
## Materials and Methods  
### Trial Field Setup  
The field trial was performed in 2021 in the northern part of the Republic of Serbia (geographic location: 45\({}^{\circ}\)34\({}^{\prime}\)24.67\({}^{\prime\prime}\) N, 19\({}^{\circ}\)50\({}^{\prime}\)36.72\({}^{\prime\prime}\) E). To enhance the understanding of the experiment, the flowchart is presented in Figure 1. The trial field encompassed a 0.53 ha area that was traditionally cultivated using the wheat-maize-soybean crop rotation. The field was characterized as being flat with minimal variations in elevation or slope.  
The experimental field was scanned using an EM-38 MK2 probe (Geonics Ltd., Mississauga, ON, Canada) to map soil electrical conductivity (EC) and soil variability for subsequent soil sampling planning. The results are presented in Table S1 and Figure S1. The corn seeds were worn using a 4-row precision seeder (Trail version, Precision planting LLC, Tremont, IL, USA) in 6 consecutive passes at a row spacing of 0.7 m. The planter was equipped with a hydraulic downforce system (DeltaForce(r)) on each row unit and finger-pickup seed meters (Precision Planting, LLC., Tremont, IL, USA). An authorized team of professionals had the responsibility for all settings (Livona Doo, Belgrade, Serbia).  
Figure 1: The illustration of the in-field activities and data processing procedures.  
The seed spacing was adjusted to 0.19 m based on the experts' knowledge and the common practice applied for the specific soil types in the region. The experimental field was divided into six (6) plots, each of which included one pass with the seeder. The seeder travel speed was the factor evaluated in the trial. The working speed remained constant during the single pass in every plot. At each subsequent pass, the speed was increased by 1 km h\({}^{-1}\). The speed range considered for data collection was from 8 km h\({}^{-1}\) to 13 km h\({}^{-1}\). The proposed hypothesis was that the variability in plant spacing could be induced by the varying speed settings, despite the seeder's sophisticated dosing and seed delivery mechanisms being engineered to mitigate this problem. When seeds are released from the seeding mechanism and allowed to fall freely, there is still a probability for a stochastic event to take place. The corn hybrid used in the research was the Pioneer 0217 AQ (Corteva Agriscience, Wilmington, DE, USA).  
The characteristics of the seed form were determined by measuring the dimensions of 240 individual randomly selected seed samples.
## 1 Introduction
### Research Gaps and Contributions  
The welfare of a P2P network depends on the type of bidding strategy and the physical parameters of the system. In a majority of the literature, (i) the network parameters are not considered while comparing the bidding mechanisms for the P2P participants; (ii) different trading strategies have not been explored while developing the constrained P2P markets; and (iii) the sensitivity of the prosumers towards the network parameters have not been discussed for modeling their trading behavior. Considering the mentioned research gaps, the proposed contributions are as follows:  
* We modeled the intermittent nature of the PV sources for P2P trading prosumers using the fractional integral polynomial method;
* We compared different trading strategies, such as the rule-based ZI mechanism and GT approach, for constrained CDA markets for trading the PV energy to analyze the impact on the individual/social welfare;* We suggested quadratic trading loss and network-impedance-based utilization fee models for the P2P markets to incorporate the network constraints;
* We designed a reluctance-based sensitivity model for the prosumers towards the network constraints of the system to highlight the trading pattern of the participants under heavy distribution losses/a network fee;
* We extended the P2P platform presented in [28] to incorporate the GT approach while considering the preference of the individuals and a sensitivity analysis of network parameters.  
The remaining paper is organized as follows: Section 2 describes the system model for the suggested P2P market. In Section 3, the details of the market structure are presented. The results and analysis for the given test cases are discussed in Section 4. The findings of this research are concluded in Section 5.
# Design and Construction of Oblique Prestressed Concrete Payement: A Case Study in China
## 3 Stress Analysis of Oblique Prestressed Concrete Slabs  
### Prestress Loss  
According to the specification of unbonded prestressed concrete structure, the effective stress of the oblique prestressed tendons can be expressed as [24]:  
\[\sigma_{pe}=\sigma_{con}-\sum_{n=1}^{5}\sigma_{ln} \tag{1}\]  
where,  
\(\sigma_{pe}\) = the effective stress;  
\(\sigma_{ln}\) = the value of prestress loss of item \(n\) (\(n\) = 1 to 5);  
\(\sigma_{con}\) = the tensioned control stress of prestressed reinforcement.  
In general, the prestress loss include the following four aspects [24; 25]:  
1. Prestress loss caused by the anchorage deformation and the shrinkage when tensioning the prestressed reinforcement, denoted as \(\sigma_{l1}\). Regardless how the tendons are tensioned, the deformation of anchorage and the base plate, as well as the sliding of the prestressed tendons can cause prestress loss. The prestress loss due to the tendon shrinkage mainly occurs around the anchorage area and is non-uniform along with the tendon direction [26]. The prestress loss due to anchor deformation and tendon shrinkage can be calculated as: \[\sigma_{l1}=\frac{a}{l}E_{p}\] (2) where,  
\(\sigma_{l1}\) = the prestress loss due to anchor deformation;  
\(l\) = the distance between the two anchor ends, mm;  
\(a\) = the shrinkage length of the tendons due to anchor deformation, for jaw vice anchorage, 6-8 mm.
2. Prestress loss due to friction, denoted as \(\sigma_{l2}\). There is friction between the prestressed tendons and the casing pipe. The closer to the anchorage area, the higher is the friction force, which can be expressed as [24]: \[\sigma_{l2}=\Big{\{}\sigma_{con}\Big{(}1-e^{-(kx+\mu\theta)}\Big{)}\quad kx+ \mu\theta>0.2\;\sigma_{con}(kx+\mu\theta)\quad kx+\mu\theta\leq 0.2\] (3) where,  
\(\sigma_{l2}\) = the prestress loss due to friction;  
\(x\) = the distance from the anchorage area to the calculation cross section;  
\(k\) = the factor of the casing pipe;  
\(\mu\) = the friction coefficient between the tendons and the casing pipe;  
\(\theta\) = the cumulative angle.
3. Prestress loss due to relaxation. The unrecovered deformation of steel wires at high tensioning is inevitable, which is also known as relaxation. The prestress loss due to this can be expressed as [24]: \[\sigma_{l3}=0.125(\frac{\sigma_{con}}{f_{ptk}}-0.5)\sigma_{con}\] (4) where, \[0.7f_{ptk}<\sigma_{con}<0.8f_{ptk}\]\(\sigma_{13}\) = the prestress loss due to relaxation; \(f_{ptk}\) = the tensile strength of the prestress tendons.
4. Prestress due to concrete shrinkage and creep. Shrinkage is a common phenomenon during the curing of concrete. Creep occurs when high pressure is applied on the concrete. Both the shrinkage and creep can cause prestress loss, which can be expressed as [24]: \[\sigma_{13}=\frac{35+280\frac{\sigma_{pc}}{f_{cm}}}{1+15\rho}\] (5) where, \(\rho\) = the reinforcement ratio; \(\sigma_{pc}\) = the normal compressive stress of concrete; \(f_{cu}^{\prime}\) = the compressive strength of concrete under pressure.
# A Multi-Frequency Omnidirectional Antenna Based on a Ring-Shaped Structure
## 3 Test and Analysis  
To verify the performance of the antenna, we use an electromagnetic computer simulation (CST) microwave studio to simulate and analyze the antenna parameters. The actual return loss (S\({}_{11}\)) of the antenna is measured by an Agilent vector network analyzer under normal experimental conditions and compared with the simulation results. First, the microstrip line of the antenna is welded to the SMA joint matching the impedance of 50 \(\Omega\). Then, the network analyzer is connected to a coaxial cable with the same impedance matching and calibrated in the order of open circuit-short circuit-load. After the device calibration, the antenna is connected with a coaxial cable and tested. The measurement of the antenna is shown in Figure 7.  
The comparison between antenna simulation and measurement results is shown in Figure 8. The experimental results show that six frequency points can reach below 10 dB, and the number of frequency points is consistent with the six frequency points of the theoretical simulation. The six frequency points measured in the experiment are 0.58 GHz, 0.93 GHz, 1.23 GHz, 1.49 GHz, 2.42 GHz, 2.61 GHz, and the theoretical simulation is 1.10 GHz, 1.33 GHz, 1.63 GHz, 1.97 GHz, 2.08 GHz, 2.69 GHz respectively. There are three reasons why these frequencies are not the same. The first reason is the size error brought by processing. The second reason is the limitation of experimental conditions, the  
Figure 6: Simulation of antenna gain.  
Figure 7: Photograph of the fabricated antenna.  
measurements were not completed in the microwave darkroom, so the electromagnetic environment affected the antenna radiation. The third reason is that the SMA joint is not a perfect match with the sample.
###### Abstract
[26] found that the seepage properties of rocks can be characterized by using the seepage capacity, diffusion rate, etc. [PERSON] et al. [27] concluded that the fracture boundary effect has a non-negligible influence on the slurry diffusion, and obtained the streamline equation for the slurry diffusion trajectory. [PERSON] et al. [28] analyzed the influence of grouting volume, grouting pressure, and other factors on the grout diffusion pattern, concluding that, under different grouting parameters, the grout diffusion radius is directly proportional to the grouting time. [PERSON] et al. [29] developed a novel dynamic grouting test system to study the impact of water flow rate, grout flow rate, and fracture aperture on grout diffusion characteristics, concluding that increasing the grout flow rate and fracture aperture can effectively control fracture formation. [PERSON] et al. [30] monitored chemical grouting experiments on porous sandstone fractures using low-field nuclear magnetic resonance technology, comparing and analyzing the effects of grouting pressure and fracture inclination on grouting volume, grouting time, and grout permeability. [PERSON] et al. [31] investigated the influence mechanism of fracture roughness on grout flow and diffusion characteristics, finding that pressure gradient and maximum diffusion velocity exhibit a parabolic growth with changes in relative roughness. [PERSON] et al. [32] conducted a study on the influence of slurry temperature on the diffusion range within planar cracks, revealing significant temperature effects that should be considered in the context of grout diffusion. The current research on the diffusion of slurry in rough rock fractures primarily focuses on the individual influence of single grouting parameters or fracture characteristics. There is a lack of consideration for the comprehensive effects of multiple parameters on slurry diffusion. Through the development of a custom experimental apparatus for slurry diffusion in rough fractures, the diffusion characteristics of slurry in rough fractures were analyzed. Simulation experiments of slurry diffusion under different grouting parameters were conducted. The research findings contribute to an understanding of the diffusion patterns of slurry in fractured rock masses and provide a reliable reference for the selection of grouting processes in different grouting environments.
## 5 Conclusions  
The paper identifies and discusses the challenges associated with multimedia transmission, including Quality of Service, resource availability, and signal interference. It emphasizes the need for efficient dissemination protocols to ensure the timely and reliable delivery of multimedia messages in dynamic vehicular environments. One notable opportunity presented in the review is the use of image transmission for image-enhanced TIMs and emergency notifications. The review paper goes beyond technical considerations by exploring the advantages and applications of image transmission in VANETs. It emphasizes the potential of image-enhanced TIMs to enhance public emergency notifications and enable comprehensive communication. The Dedicated Short-Range Communication (DSRC) architecture uses a variety of protocols to manage its functions, crucially employing the IEEE 802.11p standard at the Physical (PHY) and Media Access Control (MAC) layers. This standard manages access to the shared communication medium within DSRC networks and helps prevent collisions among vehicles. The DSRC system also efficiently allocates specific channels for control- and safety-related communications. The integration of multimedia capabilities into Vehicle-to-Everything (V2X) communication offers promising applications like in-vehicle infotainment, advanced driver assistance systems,telematics, and more. However, bandwidth limitations pose a challenge, particularly in the transmission of multimedia content.  
This paper also discusses the importance of addressing the issues related to multimedia transmission in vehicular communication systems and suggests an efficient compression method. This is crucial for the development of multimedia applications in these systems. The recommendations given, if put into practice, could greatly improve the efficiency and reliability of communication systems in vehicular ad hoc networks (VANETs). This could result in better safety, efficiency, and emergency response capabilities in changing vehicular environments. Furthermore, the paper offers a novel viewpoint, underlining the potential of image transmission to improve Traveler Information Messages (TIMs) and emergency alerts. It also highlights the significant role of Artificial Intelligence (AI) in enhancing information distribution, providing a thorough understanding of this area. This paper serves as a useful reference for both researchers and practitioners who are working on developing practical solutions for multimedia transmission in vehicular ad hoc networks.  
The authors collaborated on this research topic and collectively contributed to this work. Literature review, M.O. and B.K.; writing--review and editing, M.O. and B.K.; writing--original draft preparation, M.O.; visualization, M.O. and B.K.; supervision, B.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
[1]**Data Availability Statement:** Further information presented in the study is available on request from the corresponding author.  
[2]**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
This work focuses on small bearingless permanent magnet synchronous motors (BPMSMs). In order to enhance its torque control stiffness and improve the stability of its torque and magnetic levitation force dynamic waveforms, a novel six-phase distributed single-winding BPMSM with 12 slots and 2 poles (six-phase DSW-12/2-BPMSM) is proposed and researched in this work. First, the structure and working principle of the six-phase DSW-12/2-BPMSM are analyzed. Subsequently, considering the relative permeability of permanent magnets, mathematical models of the inductance matrix, electromagnetic torque and radial magnetic levitation force are established. Then, using the finite element method (FEM), the control characteristics of the electromagnetic torque and magnetic levitation force of the six-phase DSW-12/2-BPMSM are analyzed, and the mathematical model is verified. Finally, FEM simulation analysis and comparisons are conducted with a commonly used six-phase centralized single-winding BPMSM with 6 slots and 2 poles (six-phase CSW-6/2-BPMSM). The research results show that the established mathematical model is effective and accurate compared with the six-phase CSW-6/2-BPMSM. The six-phase DSW-12/2-BPMSM has greater torque control stiffness, its dynamic waveforms of torque and radial magnetic levitation force have higher quality and stability, and the coupling degree between its torque and radial magnetic levitation force is lower.  
xxxxxx-phase BPMSM; distributed single-winding; mathematical modeling; finite element analysis; electromagnetic characteristics analysis +
Footnote †: journal: Applied sciences
## 3 Joint Cooperative Spectrum-Sharing Scheme  
### Utility of \(EU_{m}\)  
During phase 1, \(EU_{m}\) transmits its data to \(DT_{n}\) and BS. After successfully receiving \(EU_{m}\)'s data, \(DT_{n}\) conveys both the \(EU_{m}\)'s data and its own data to BS and \(DR_{n}\), with the aid of the NOMA technology during phase 2. Provided that the BS successfully receive the data from \(DT_{n}\), SIC is invoked at the BS for separating the \(EU_{m}\)'s data and \(DT_{n}\)'s data. Then the BS combines the direct transmission from \(E\!U_{m}\) within phase 1 and the relayed transmission from the \(DT_{n}\) during phase 2 with the aid of MRC. Hence, the utility of \(E\!U_{m}\) may be expressed as:  
\[U_{mn}^{E\!U}(\tau_{mn},\alpha_{mn})= R_{mn}^{C}=(1-\tau_{mn})\log_{2}(1+SINR_{MRC})\] \[= (1-\tau_{mn})\log_{2}\left[1+\frac{h_{bn}\left(1-\alpha_{mn} \right)P_{n}}{ah_{bn}\alpha_{mn}P_{n}+N_{0}}+\frac{h_{bn}P_{m}}{N_{0}}\right] \tag{11}\]