esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Improving Satellite Positioning Precision Based on Multi-Frequency Navigation Signals
## 2 Methodology and Equations
### Positioning Solution of Multi-Frequency Receiver
 \tag{18}\]where \(\rho_{ij}\) is the pseudo-range observation value; \(\rho_{i}(0)\) is the pseudo-range value predicted by the initial point, which can be expressed as:  
\[\rho_{i}(O)=\sqrt{\left(X_{0}-X_{i}\right)^{2}+\left(Y_{0}-Y_{i}\right)^{2}+ \left(Z_{0}-Z_{i}\right)^{2}} \tag{19}\]  
where \(\left(X_{0},Y_{0},Z_{0}\right)\) are the coordinates of the initial point, and (\(X_{i}\), \(Y_{i}\), \(Z_{i}\)) are the position coordinates of the i-th satellite. The estimated value of the offset calculated by the least square algorithm is:  
\[dX= \begin{bmatrix}\Delta X\\ \Delta Y\\ \Delta Z\\ \Delta\delta t\end{bmatrix} =\left(H^{T}H\right)^{-1}H^{T}B \tag{20}\]  
Add the current offset to the initial value to get the initial value of the next iteration (\(X_{0}+\Delta X\), \(Y_{0}+\Delta Y\), \(Z_{0}+\Delta Z\)). After reaching the threshold, stop the iteration and get the current receiver position estimate.
## 3 Experiments and Analysis
### The Experiment of Training and Testing LACB_ECBAM Net  
When the LACB_ECBAM Net model is trained, different batch sizes and initial learning rates can lead to changes in the output results of the model. To discover how the training batch size setting and the initial learning rate setting affect the training effect of LACB_ECBAM Net, this experiment compared the training accuracy of the network under different settings using control variables to select the most suitable training batch size and initial learning rate. In addition, the experiment used cross-entropy as the loss function and was optimized using the better-performing Adam gradient descent optimizer with the default decay rate.  
The initial learning rates were set as 0.0005, 0.001, and 0.0015, respectively. After four epochs, the learning rate fell by 0.65 times the current rate as they all followed the learning rate reduction method with a fixed stepping length. A total of 20 epochs were trained, and each epoch was then fed to the model as a test set to test the training effect of the current epoch and recorded. Figure 10 shows the influence of different initial learning rates on the accuracy of network training under the condition that the batch sizes are set to 16, 32, and 64.  
According to the analysis of the variation trend and convergence of LACB_ECBAM Net model accuracy in the figure, the initial learning rate suitable for LACB_ECBAM Net model training is 0.001 and the batch size is 32. On the same condition, an overhigh initial learning rate will accelerate the convergence rate of accuracy in the early stage, but it will easily fall into the plateau of repeated jumping in the later stage and affect the final accuracy. A too-low initial learning rate will lead to the overall convergence rate being slow and not meeting the experimental requirements. Similarly, setting the batch size too large or too small can also negatively affect the training effect of the model. At the same time, it can be found that if appropriate training parameters are set, the accuracy of LACB_ECBAM Net will eventually converge to 99.957%. This shows that LACB_ECBAM Net has an effective damage identification ability for WTB, which can meet the needs of practical engineering.
# Light-Weighted Password-Based Multi-Group Authenticated Key Agreement for Wireless Sensor Networks
## 4 Security Analysis  
The security analyses on the correctness, session key security, perfect forward security, mutual authentication, and privacy protection are provided; it also resists password guessing, known-key attacks, and sensor node capture attacks.
# Experimental Damage Identification of a Model Reticulated Shell
## 5 Conclusions  
For the damage identification of a reticulated structure, the proposed damage identification method based on the AR model and the L-M algorithm was verified by experiments on a model structure. The major conclusions of the study are summarized as follows:  
1. The AR model was established based directly on the acceleration responses of the nodes. Using the coefficients of the AR model, the damage sensitive features were defined. Taking the damage sensitive features as the inputs and the damage locations as the outputs, an improved back-propagation neural network was established based on the L-M algorithm. The trained network can be used to detect the damage location of a reticulated shell. The proposed damage detection method does not require modal parameter identification and excitation information. It improves the accuracy of damage identification to some extent.
2. According to the principle of equivalent stiffness, we simulated the structural damage by replacing cross-sectional elements with smaller sized elements. The experiment scheme of single-point excitation and multi-point responses was adopted. Then, we conducted impact experiments on a K6 single-layer spherical shell with a scale of 1/10. The experimental results showed that the damage detection method based on the AR model and the L-M algorithm improved back-propagation neural network accurately identified the damage and the location of the damage that occurred in the reticulated shell.
3. Accuracy of the damage identification method is influenced by some factors, such as the sensor placement, neural network structure, and training algorithm of the neural network. In this paper, the sensor placement was optimized based on the particle swarm optimization algorithm.
4. The L-M algorithm was used to train the back-propagation neural network, which has the characteristics of faster training speed and better local searching ability. In addition, the accuracy of damage identification is effectively improved.  
This research was supported by the National Natural Science Foundation of China (Project No. 51068019), the Provincial Natural Science Foundation of Shandong (Project No. 2R2013 EEL013), the Provincial Natural Science Foundation of Hubei (Project No. 2013 CFB399), and the International Cooperation Program for Excellent Lectures funded by the Shandong Provincial Education Department.  
[PERSON] designed the experiment and wrote part of the paper; [PERSON] and [PERSON] conducted the experiment and analyzed the data; [PERSON] conceived the idea; [PERSON] wrote part of the paper and proofread the entire paper; [PERSON] wrote the paper and offered critical comments.  
The authors declare no conflict of interest.
# A Novel Secondary Side Series LCD Forward Converter with High Efficiency and Magnetic Reset
## 5 Simulation and Experimental Analysis  
### Simulation Analysis  
To verify the theoretical analysis, a PSIM simulation was built. The simulation results of the proposed converter were obtained from PSIM to verify the theoretical analysis preliminarily, and the index requirements of the experimental prototype are shown in Table 1.  
According to the index requirements of the experimental prototype, assuming that the maximum duty cycle of the converter was 0.45, the number of turns of the primary winding turns was \(N_{1}=30\), the number of turns of the secondary winding was \(N_{2}=13\), and the excitation inductor \(L_{\text{m}}=3.5\) mH when the minimum DC input voltage was 198 V. According to Equation (12), we obtained \(0<C_{2}<25\) nF.  
Considering the impact of the actual capacitance and the PCB parasitic capacitance, the capacitor \(C_{2}\) was taken as 10 nF in this experiment. According to Equation (17), we obtained \(0<L_{2}<162.5\) uH. Considering its volume and winding loss, \(L_{2}=45\)uH was selected in this experiment. Under the condition that the inductor \(L_{2}\) was already present, the inductor \(L_{1}\) was taken as 50 uH, according to Equation (37).  
Figure 7 shows the key waveforms of the proposed converter with the output currents of 2 A, 5 A, and 10 A, respectively. As shown in Figure 7a, the inductor current waveforms \(i_{L1}\) and \(i_{L2}\) affirmed that the inductor \(L_{1}\) and \(L_{2}\) operated in the CCM. The voltage waveform \(V_{\text{ds}}\) affirmed that the voltage across the capacitor C\({}_{2}\) increased first and then decreased during the switch-on period, indicating that \(L_{\text{m}}\) operated in the DCM. Using the voltage waveform of \(V_{\text{ds}}\), we concluded that the proposed converter achieved a low-voltage turn-on of the switch. Similarly, as shown in Figure 7b,c, it can be concluded that the inductor \(L_{1}\) and \(L_{2}\) also worked in the CCM, and the voltage stress waveform of \(V_{\text{ds}}\) also increased first and then decreased during the switch-on period. Therefore, the excitation inductor \(L_{\text{m}}\) worked in the DCM, which was in good agreement with the theoretical results.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Input voltage & AC 165–265 V \\ Input frequency & 40–60 Hz \\ Switching frequency & 100 kHz \\ Maximum efficiency & \(\geq\)90\% \\ Output ripple & \(\leq\)1\% V\({}_{\text{o}}\) \\ Output voltage & 48 V \\ Output current & 10 A \\ Load adjustment rate & \(\pm\)2\% \\ \hline \end{tabular}
\end{table}
Table 1: The main design indicators of the experimental prototype.
## 1 Introduction
Furthermore, specific control functions within MGs are derived from generic control methodologies, such as closed-loop controls, open-loop controls, logic-based controls, or optimization techniques, as shown in Table 1. Notably, optimization techniques are gaining increasing attention in research, with sub-fields like linear programming, nonlinear programming, and metaheuristic optimization (e.g., genetic algorithms) emerging as focal points. This diversification reflects the ongoing exploration and refinement of control methodologies in the domain of electric PSs and MGs [20; 21]. This paper investigates the problem of ancillary service allocation among all available resources within an MG. As most of the previous work related to this topic rely on single-bus models of MG (Table 1), meaning that power losses, power flows, voltage profiles, and their limitations are all neglected, this paper proposes a detailed MG modeling and quantifies the importance of having a detailed model by conducting a comparative analysis. Even though voltage profiles have not been explicitly analyzed in this research paper, voltages are calculated at each step of the optimization and have a direct impact on power losses in the grid, which in turn affects the optimization results--something that cannot be captured in a single-bus model. In short, even though the considered ancillary service requirement primarily involves power changes, the allocation of this requirement among different resources is significantly influenced by voltage profiles. In addition, the proposed model introduces Virtual Load at the point of common coupling between the power grid and MG as a way of including ancillary service signals coming from the system operator into power flow analysis. The proposed methodology is validated using the CIGRE medium voltage (MV) microgrid benchmark model [22]. Findings suggest that the inclusion of detailed MG modeling can influence the outcomes by 11%. In addition, Virtual Load has been successfully incorporated into load flow analysis, providing optimal results with the inclusion of ancillary services.
# Active Optics and Aberration Correction Technology for Sparse Aperture Segmented Mirrors
## 2 Sparse Aperture Segmented Mirror Aberration Evaluation and Active Optical Control
### Segmented Mirror Active Optical Control Mode
#### 2.2.1 Control Model Based on the Variation of the Edge Sensor Position
 \tag{14}\]  
By defining the sensor reading \(H\) as the difference in heights of the sensor positions between two adjacent segments, they can be written as follows. \[H=\sum_{i=1}^{6}C_{i}z_{i} \tag{15}\]  
When the hard spots of two adjacent segments are arranged differently, \(C_{i}(i=1,\cdots,6)\) is also different. When the segmented mirrors are subjected to wind, gravity, and temperature, the sensor height difference between the segment becomes \(H_{i},i=1,\cdots,24\). The corresponding hard spot height becomes \(z_{i},i=1,\cdots,21\), which gives the following relation. \[A_{24\times 21}\cdot z_{21\times 1}=H_{24\times 1} \tag{16}\]  
where \(A\) is the stiffness matrix between the hard spot \(Z\) directional displacement and the variation in the position of the edge sensor. Let the solved residuals of \(H\) are represented as \(v_{H}\), then it can be written in the following fashion. \[v_{H}=A\hat{z}-H \tag{17}\]  
where \(\hat{z}\) is the estimated value of \(z\). \(\hat{H}=A\hat{z}\) is the estimated value of \(H\). the evaluation function of the correction quantity is represented as \(MSE(\hat{H})\).
# Network Tower Sharing Analysis in Greece: A Structure-Conduct-Performance Approach
## 3 Results
### Government Policies--Conduct--New Market Structure
#### 3.3.1 Government Policies  
Government policies regarding infrastructure sharing in the Greek telecommunication industry come from Hellenic Telecommunication and Post Commission (EETT), whose policies are described in the current subsection. More precisely, infrastructure-sharing policies started to be applied back in 2011 when 900 MHz and 1800 MHz licenses involved an obligation for MNOs to negotiate and develop commercial agreements upon another MNO's request, in exact cases of collocation and sharing of equipment and/or real estate (including passive components). In that respect, MNOs could sign commercial agreements between them, which should not restrict competition (in accordance with Law 3959/2011 on the Protection of Free Competition) and should notify EETT 90 days in advance to obtain approval. On the other hand, EETT would provide approval, ensuring that the competition's regulations have been followed. Of course, setting no restrictions on the commercial terms of collocation made negotiations a \"tool\" often used by the incumbent to discourage and/or avoid any infrastructure sharing by setting terms difficult to be accepted by any competitor, i.e., an alternative MNO.  
It was only after 2012, when Law 4070 applied, that EETT could set new regulations requiring MNOs to collocate masts and antennas mainly for environmental reasons, when technically possible, upon request, and on reasonable terms. An overall change in how operators faced infrastructure sharing and its benefits appeared when an agreement between Vodafone and WIND came to EETT for approval on July 2013, regarding network infrastructure sharing for mobile 2G/3G. Approval was given under the condition that EETT reserved the right to check and ensure that no law violations were conducted, or competition conditions were not violated.  
Following infrastructure-sharing policies of 2011 and considering the negative response of the market as far as cooperation agreements amongst MNOs are concerned, EETT amended a regulation in 2015, encouraging collocation between MNOs, but on a basis of prefixed and mandatory provisions for any type of agreement. The procedure for improvements on this regulation continued, with amendments and codification being set on public consultation during 2018.  
In November 2020, EETT approved (unconditionally cleared) v to proceed with the acquisition of sole control over the passive mobile network infrastructure of WIND, leading MNOs to rearrange their conduct as it will be described in the next section.  
EETT continued to improve the relevant regulation for co-location and joint use of facilities, conducting a second round of public consultation in 2022, in accordance with the provisions of Law 4727/2020. As a result, an amended \"Regulation for the co-location and joint use of facilities\" was prepared by EETT, which was published in 2023.
## 1 Introduction
### Application Plane
The SDN disjoints the control layer from the information layer, and the general administration and coordination of the system assets are completed through a programmable controller. This, accordingly, encourages empowering a seller-free control of the whole system for both system bearers and undertakings, thus extensively streamlining the system plan and tasks and establishing frameworks for an exceptionally adaptable and programmable system administration foundation. Subsequently, by providing a programmable SDN-based controller, it is simpler to arrange unique system gadgets and convey a wide cluster of new applications immediately [12]. Nevertheless, in spite of a few favorable circumstances that SDNs bring to a systems administration framework, it is additionally defenseless against various security attacks since noxious elements may dispatch the attacks on either the information plane by means of focusing on the system components from inside the system itself and by means of the SBI API, by legitimately attacking the control layer as it goes about as they brought together purpose of knowledge for the whole fundamental system or on the applications plane by focusing on assured specific applications, and programs by the NBI API [13]. However, guaranteeing security in an SDN-based VANET is out of the scope of this article. Although various models have been as of late proposed to ensure an improved system asset, a large portion of the executives in VANETs have not represented the one of a kind VANETs related characteristics and qualities in their structures (i.e., visit deviations in organize topologies inferable from the exceptionally  
Figure 3: SDN architecture modes, technologies, and opportunities. unique conduct of vehicles in the information plane, very enormous and disseminated organization, stringent postponed imperatives, the requirement for effective and smooth handovers, and so forth) [14]. In addition, some of these designs essentially depend on gathering unified insight in an SDN-based controller, which from one perspective, gives the global perspective on the whole hidden system, yet then again, may turn into a solitary purpose of system disappointment if there should be an occurrence of any unexpected occasion. Therefore, a re-structure of the current vehicular systems administration design is extremely fundamental [15].
# Bacterial Indole-3-Acetic Acid Influences Soil Nitrogen Acquisition in Barley and Chickpea
## 2 Results
### IAA Biosynthesis in Klebsiella Contributes to Enhanced Root Traits  
To understand the effect of diazotrophs on the root system of barley and chickpea, matured plants were harvested after 90 days (Figure S3) and roots were gently washed with water to remove adhered soil. Washed and drained-to-dry roots were then studied for six parameters, namely root length (RL), root fresh weight (RW), root dry weight (RDW), root diameter (Rootdia), root surface area (RSA), and the number of secondary roots (NSR). The results are described in Table 1. In general, the structural parameters of root in both plant types were positively altered by the wildtype strains. In barley, all the treated plants except _\(\Delta\)nifH_ showed superior phenotype than the control plants. Amongst all the traits, RSA and NSR were the most affected parameters. Compared to control, _K.p._ SSN1 showed profound  
Figure 1: Indole-3-acetic acid (IAA) production and acetylene reduction in four strains. Control is the value of uninoculated media. Bars represents mean values of replicates. Error bars represent standard error from the mean (\(n=5\)).  
effects on all the structural traits, specifically with eightfold and threefold increment in the RSA of barley and chickpea, respectively. The inoculation of chickpea with _K.q._ SGM81 resulted in maximum NSR increased by 113% and 60% than the control in barley and chickpea, respectively. The overall impact of different treatments in terms of their positive effect on barley and chickpea root can be summarized as _K.p._ SSN1 > _K.q._ SGM81 > _K.o._ M5a1 > control > _\(\Delta\)nifH_, as shown in Table 1. Compared to the non-inoculated, plants treated with the _nifH_ mutant showed a decrease in the root traits. Since M5a1 \(\Delta\)nifH_ has neither beneficial IAA production capacity nor fixes nitrogen, the most likely explanation is that the high titer of M5a1 \(\Delta\)nifH may partly exclude other (e.g., IAA microbes) soil-endogenous soil microbes in the rhizosphere from providing beneficial services to the plant. Plants under normal growth conditions establish a mutualistic relation with the soil flora, which could have been affected due to abundance of _nifH_ mutant cells. Taken together, these results imply that _Klebsiella_ strains capable of auxin production (_K.p._ SSN1 and _K.q._ SGM81) could largely be promoting enhanced root growth and hence more nutrient access compared to non-auxin producers (_K.o._ M5a1 and \(\Delta\)nifH_).
# LEO-Augmented GNSS Based on Communication Navigation Integrated Signal
## 3 Performance of Communication Signal  
The influence of navigation signal on communication signal mainly contains two aspects: the BER performance and the communication capacity loss.  
For method 1, the navigation signal becomes interference to the communication signal, and it decreases the BER performance. Therefore, the power of the navigation signal should be limited to guarantee the BER requirement. Method 1 does not cause any capacity loss.  
For methods 2 and 3, the bands of navigation signal and communication signal are separated. The power of navigation signal would not affect the BER performance of the communication signal. However, the cost is communication capacity loss.
## 3 Simulation Modeling and Verification
### Creation of a Surface-Fitting Contact Model
#### 3.1.2 Analysis of the Fitting Contact Force Between the SG Flexible Tool and the Marble Surface  
To maintain a constant contact length (L) when the SG flexible tool is in contact with marble surfaces of different curvature, the polishing force applied to the SG flexible tool differs with each curvature. Following the simulation of the contact area discussed in Section 3.1.1, force was applied to all contact points that were extracted and synthesized  
Figure 6: Simulation of the contact area between the SG flexible tool and the marble surface.  
to obtain the reactive force of the marble surface on the SG flexible tool. According to the principle of force interaction, the reactive force is also the normal force component of the externally applied force \(F\)) on the SG flexible tool. The calculation of the polishing force (\(F\)) required for polishing marble surfaces of different curvatures can be performed using Equation (1).  
\[F=F_{n}/cos\theta \tag{1}\]  
Variables:  
\(F\): denotes the force externally applied to the SG flexible tool.  
\(F_{n}\): represents the normal component of \(F\)  
\(\theta\): denotes the contact angle between the SG flexible tool and the workpiece.  
Figure 7 shows the relationship between the polishing force (F) and curvature when the marble surfaces of different curvatures are polished with the same contact length (L). The relationship between the polishing force (F) and the curvature of different parameters was fitted, as indicated by the dashed lines in each graph. The R\({}^{2}\) values after fitting are consistently close to 1, indicating a high degree of fit.