esa-sceva/satcom-chunk-collection huggingface dataset:
# Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies
## 1 Introduction
Moreover, the proposed strategies attain better probability of detection than the existing works when other scenarios are employed, such as varying the number of CRs in a cluster, increasing sensing time and varying fusion parameters. The contribution of this work can be summarized as follows:  
* Proposing a framework of combining hard decision, i.e., one-bit, and softened-hard decision, i.e., two-bit, using three different threshold strategies to improve the detection performance of a CRN with multiple clusters while reducing the reporting overhead over the control channel between FC and CHs. * Investigating the impact of increasing sensing time, and number of CRs in each cluster to verify the robustness of the proposed strategies. * Investigating the impact of fusion parameter \(K\) on the performance of the proposed strategies. For this purpose, for \(N\) users, different values of \(K\) have been used, i.e., half voting \(K=\lceil\frac{N}{2}\rceil\), majority voting \(K=\lceil\frac{N}{2}\rceil+1\), and minority voting \(K=\lceil\frac{N}{2}\rceil-1\), where the most existing works used only the majority voting rule. Note that this work is different from our previous works [16; 17] where our work in [17] combines EGC and SC as a soft decision rule with one-bit hard decision rule over the hierarchical structure of the clusters, while our work in [16] proposes a weighting strategy to combine two-bit and one-bit rule using different weights, where FC assigns a weight for each cluster based on its estimated SNR. The rest of the paper is organized as follows. Section 2 provides a background of cognitive radio network. Section 3 provides a description of the system model. Section 4 describes the proposed combination algorithms. Section 5 shows how to compute the reporting overhead. Section 6 illustrates and discusses the results of implementing the proposed algorithms.Finally, Section 7 presents the conclusions.
## 7 Discussion  
### Content Dissemination  
In this article, based on the content dissemination, the network is divided into three levels: content domain, data path, and node. In the process of hierarchy, we mainly consider two processes: content from a server to user and request from users to server. The actual content distribution process is very complex. For the cache size allocation to fit the content distribution process more closely, some caching system models are needed to analyze the content distribution process in more detail.
## 6 Simulation Results  
We validate and evaluate our proposed scheme via simulations, where our proposed AEDG protocol is compared to two existing protocols: AURP and AEERP. We vary the network area between 100 m \(\times\) 100 m and 10 km \(\times\) 10 km, where 10 and 100 nodes and one AUV are deployed. The nodes are initially equipped with \(100\) J. We assume a packet size of 70 bytes. Each sensor node has a fixed transmission range of \(20\) m. The LinkQuest UWM1000 [20] acoustic modbit rate. The AUV moves on the sub-optimized elliptical path. Simulation parameters are given in Table 2. We use the following metrics for the performance evaluation.
## 2 Mathematical Formulation
### Model Constraints  
The operation of the DG and BESS within an AC MG is subject to constraints based on equipment limitations and network operation. These include loadability and voltage limits, power balance, and the minimum and maximum active power output of DG and BESS operation. It is worth noting that, in this study, only active power injection from the DG is considered, as this reflects their typical mode of operation.
## 2 Experimental Procedure  
### Instrumentation  
A Tescan Field Emission Scanning Electron Microscope (FE-SEM, Mira3-Xum, Brno, Czech Republic) was used to capture FE-SEM images of the polypyrrole nanotubes. A PAN analytical instrument (x,pert3, Almelo, The Netherlands) was applied to obtain the X-ray diffraction (XRD) pattern of the polypyrrole nanotubes. Moreover, Fourier transform infrared spectroscopy (FT-IR) was carried out on a Bruker spectrometer with KBr pellets (Tensor-2, Bruker Corporation, Hanau, Germany) and was applied to obtain the functional groups of the polypyrrole nanotubes. An Autolab electrochemical instrument (Metrohm-PGSTAT302N, Metrohm Autolab B.V., 3526 KM, Utrecht, The Netherlands) was used for the voltammetric measurements. The experimental settings were explored using General Purpose Electrochemical System (GPES) software. The three-component DropSens screen-printed electrode (DRP-110, DropSens, Oviedo, Spain) consisted of a graphite working electrode, a silver pseudo-reference electrode, and a graphite counter electrode. All pH adjustments were performed using a Metrohm 710 pH meter (Metrohm Autolab B.V., 3526 KM, Utrecht, The Netherlands).
## 1 Introduction
Thus far, scientific work by research has highlighted mainly the technical aspects, potential societal and policy impacts of modern technological advancements in the automotive industry, for example, as discussed by [3; 9; 10; 11; 12] and many others. However, to the best of the authors knowledge, their impact on highway or toll operators has mostly been neglected and very little work has been examined in this direction [7; 10; 11]. This study is the starting point for understanding and exploring the organizations' point of view on how innovative technologies would affect highway operators, toll agencies and highways in the future [12]. More precisely, this study tries to find the answers to the following questions:  
1. What is the expected maturity time line of innovative technologies (internet of things, big data, and autonomous vehicles) for highway applications and how would they affect the business paradigm for highway operators? 2. What is the expected percentage of autonomous vehicles on highways until 2030? 3. What would be the impact of innovative technologies (big data, Internet of things, connected vehicles and autonomous vehicles) on highways and highway operators? 4. Which potential areas require critical initial investments to facilitate autonomous vehicles on highways? The purpose of this research is to identify the time it would take for above mentioned technologies to be ready for implementation by highway operators. It is important, as it would facilitate the policy makers for such organizations in long term organizational planning, redefining and reshaping the existing code of conduct. Furthermore, it tries to identify their potential benefits, allowing highway operators to plan for and avail all the opportunities that come with technological advancements. Moreover, this study attempts at making highway operators aware of the challenges they might face in the future.
## 5 Numerical Analysis Results and Comparison of Strong Wind Analysis with Real Tornado Action
### High Wind Speed Load, T1, T2, T3  
#### 5.2.1 Structure Deformation Analysis  
Another possible mode of failure may occur as a result of structural deformation, which includes horizontal displacement and vertical deflection. The diagram below shows the deformation of all elements (Figure 12a) and the detailed deformation of the damaged element depending on the wind direction (Figure 9) as follows: east W1 (Figure 12b), north-east W2 (Figure 12c) and south-east W3 (Figure 12d).  
The exact displacement values are shown in Table 4. The data have been divided according to the type of load (permanent load or wind load, which has been divided according to speed, starting from 22 m/s (LWS) to 55.13 (T3)). The results for each wind direction are shown separately (see Section 4.3.1).  
The largest displacement obtained was for the speed of 55.14 m/s in the W2 direction, corresponding to Figure 12b.  
Figure 11: Diagram of internal forces (shear force, bending moment, stresses) for the extreme structure resulting from the acting of dead load and gusts of wind. (**a**) shear force from dead load, (**b**) shear force from gusts of wind, (**c**) bending moment from dead load, (**d**) bending moment from gusts of wind, (**e**) stresses from dead load, (**f**) stresses from gusts of wind.
## 6 Conclusions  
In this paper, an improved TF analysis method based on RSPWVD has been proposed to detect sweep interference for GNSS receivers. In order to prove the advantages and effectiveness of the developed technique, a comprehensive performance comparison has been carried out compared with the existed TF analysis approaches. The experiments have been performed on GPS L1 signals in the disturbing scenario in order to support the theoretical analysis among the aforementioned TF distributions adopted in interference detection for GNSS receivers.  
From the analysis results, the spectrogram approach presents the TF resolution trade-off problems and provides poor localization properties in the TF plane; the interference detection based on WVD presents a severe cross-interfering term problem due to the interaction of different frequency components, which makes proper disturbance interpretation impossible. Then, the SPWVD method is adopted to partially mitigate the cross-terms present in the bilinear TF distribution, since a two-dimensional smoothing function is added in both the time and frequency domains, but the disadvantage of using filtering windows is the degradation of time and frequency resolution.  
In order to eliminate the cross-term problem and preserve good time and frequency resolution in the TF plane at the same time, an improved joint TF distribution by using RSPWVD has been firstly proposed in interference detection for GNSS receivers. The interference detection based on joint  
Figure 12: Joint TF analysis by adopting RSPWVD for the GPS L1 signal with sweep interference. \(C/N_{0}\) = 46 dB-Hz, JNR = \(-\)1 dB.  
TF distribution by using RSPWVD efficiently combines the removal of the cross-interfering terms provided by a two-dimensional smoothing kernel function and an increased TF concentration of the auto-terms of the signal components achieved by the reassignment. From the analysis results, the proposed interference detection method based on RSPWVD successfully overcomes the cross-term problem and, meanwhile, presents good TF localization properties, which provide much improved interference detection performance in comparison with the existing TF analysis approaches.  
The developed interference detection technique based on joint TF analysis by adopting RSPWVD has been proven to be suitable to improve the interference detection performance in the jamming environments, which is promising for adoption in the anti-interference GNSS receiver design for civil aviation and military purposes, particularly in disturbing environments.  
This research work is supported by the Scientific Research Foundation for Returned Scholars, Ministry of Education of China.
## 2 MIMO Antenna Design Approaches
### Total Active Reflection Coefficient (TARC)  
The TARC of the MIMO system is defined as the ratio of the total reflected power from the radiating elements to the total incident power on the patch. The expression for the generalized TARC for an \(N\)-port MIMO antenna is given by Equation (6) [10]:  
\[\text{TARC}=\frac{\sqrt{\sum_{i=1}^{N}\abs{b_{i}}^{2}}}{\sqrt{\sum_{i=1}^{N} \abs{a_{i}}^{2}}} \tag{6}\]  
Here, \(b_{i}=[s]\cdot a_{i}\); \([s]\), \([b]\) and \([a]\) are the scattering matrix, the scattering vector and the excitation vector, respectively.  
Further, for two-port MIMO antennas, TARC can be given by Equation (7):  
\[\text{TARC}=\frac{\sqrt{\left(\abs{\left(S_{11}+S_{12}e^{i\theta}\right)}^{2}+ \abs{\left(S_{21}+S_{22}e^{i\theta}\right)}^{2}\right)}}{\sqrt{2}} \tag{7}\]  
The TARC is the ratio of the reflected power and the incident power, and it is expected that all the power should be accepted by the antenna. Therefore, ideally the TARC value for the MIMO antenna should be zero. While designing the MIMO antenna, one has to optimize the values of all the antenna diversity parameters within the aformentioned predefined limit.  
In this review paper, the MIMO designs are categorized based on the number of elements used and the type of characteristics demonstrated by the antenna. Most of the MIMO antennas available in the literature belong to UWB, dual band or circularly polarized types and are classified in Figure 1. The details of each type of MIMO design are further described in the following sections.
## 4 Case Study and Results Discussion
### Analysis 2: Covariate Selection Sensitivity
#### 4.3.2 Results Discussion  
Based on the analysis of both the covariate selection process and the resulting policies, several insightful findings emerge.  
In examining the policies, the importance of covariate selection in shaping the decision rule for maintenance policy becomes evident. The number and choice of covariates significantly influence the approach to equipment intervention. For instance, in models A and C, a limited selection of covariates led to an overly aggressive policy, prompting unnecessary interventions. In contrast, selecting less appropriate covariates, as in model B, resulted in a more lenient policy, thereby increasing the risk of premature equipment failure.  
In relation to the covariate selection procedure proposed by the framework, models D, E, and F emerged as interesting cases. Despite differences in their selection methods, these models yielded practically identical results. This could suggest that while the strategy for optimizing covariate weights in model E generated good results, it might not be necessary, as models with more covariates produced identical outcomes. However, a more detailed analysis revealed that models D and F included the covariates selected by model E. Therefore, it was inferred that these were the minimum covariates that must be considered to ensure adequate and feasible maintenance policies. Nevertheless, models D and F illustrated the substantial value provided by the covariate weight estimation process, as it appropriately assigned importance to the covariates to generate pertinent policies.  
As observed in this analysis, a systematic approach to covariate selection can lead to building a more parsimonious model. In this instance, this was achieved through the combined use of cross-validation and the coefficient of variation for feature selection in the correlation analysis, yielding results similar to the reference case.  
Furthermore, the results suggest that a valid strategy within the proposed framework is to select all covariates and let the optimization engine determine the relative importance of each. Notably, this approach demonstrated identical performance to model E and case 0.
## 4 Results and Discussion
### Internal Force of Immersed Tunnel  
#### 4.3.1 Axial Force and Bending Moment of Immersed Segments  
The axial force and bending moment of immersed segments in the numerical model were extracted and analyzed (Figures 18 and 19). The axial force refers to the axial force along the longitudinal \(y\)-axis of the tunnel, the transverse bending moment is the bending  
Figure 16: Relative displacement of segment joint: (**a**) horizontal; (**b**) longitudinal; (**c**) vertical.  
moment rotating around the \(z\)-axis, and the vertical bending moment is the bending moment rotating around the \(x\)-axis. Axial force and bending moment refer to the axial force and bending moment of the entire section of immersed segment, respectively.  
It can be determined that the tunnel structure is mainly subjected to tensile force in the axial direction, which is consistent with the discussion in Section 4.2.2. A change in incident angle affects the axial force and bending moment of tunnel structure. When the incident angle is within 15\({}^{\circ}\), the axial force of the structure changes little; however, with the further increase in the incident angle, the axial force of the structure increases significantly. When the incident angle is close to the critical angle, the maximum value is 36.5 \(\times\) 10\({}^{6}\) N, which is 90% higher than that of the corresponding vertical incidence. However, with the increase in incident angle, the transverse bending moment and the vertical bending moment of the tunnel structure have completely different effects. The transverse bending moment  
Figure 19: Curves of bending moment of segment at different angles of incidence: (**a**) transverse; (**b**) vertical.  
Figure 17: Horizontal and vertical displacements of segment at different angles of incidence: (**a**) horizontal; (**b**) vertical.  
Figure 18: Curves of axial force of segment at different angles of incidence.  
of the structure decreases as a whole, while the vertical bending moment increases as a whole. The peak value of the transverse bending moment is 253.3 \(\times\) 10\({}^{6}\) N\(\cdot\)m at an incident angle of 0\({}^{\circ}\), while the peak value of the vertical bending moment is 147.5 \(\times\) 10\({}^{6}\) N\(\cdot\)m at an incident angle of 30\({}^{\circ}\). The reason is that the increased incident angle reduces the horizontal component and increases the vertical component of the seismic wave. The stiffness of the immersed tunnel joint is relatively small; thus, the seismic design should pay attention to the design of the joint to prevent failure of the immersed tunnel caused by the excessive joint angle.
## 2 Materials and Methods
### Input Data
#### 2.2.3 AmeriFlux Data  
AmeriFlux ([[https://ameriflux.lbl.gov/](https://ameriflux.lbl.gov/)]([https://ameriflux.lbl.gov/](https://ameriflux.lbl.gov/))) half-hourly or hourly GPP mean values were used to calibrate and validate the LUEc. GPP mean values were calculated from AmeriFlux half-hourly or hourly averages of net ecosystem exchange (NEE, umol m\({}^{-2}\) s\({}^{-1}\)) and ecosystem respiration (Re, umol m\({}^{-2}\) s\({}^{-1}\)). At all non-Nebraska sites NEE was assumed equal to canopy carbon dioxide flux (Fc, umol m\({}^{-2}\) s\({}^{-1}\)) as storage term calculations were not provided in these datasets and are likely small for these crop systems. NEE values were screened employing light response relationships. Values were graphed in relation to concurrent AmeriFlux measured half-hourly or hourly averages of PAR (umol m\({}^{-2}\) s\({}^{-1}\)) and a nonrectangular hyperbola model fitted to a period of five days. Outliers, typically more than three standard deviations from the model, were removed. For field years in which PAR was not provided, Equation (3) was used to approximate incident PAR fluxes using values of incoming solar radiation (Rg). Interpolation of daytime NEE and nighttime AmeriFlux measured ecosystem respiration (Re, \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)) were then conducted. Half-hour averages were converted to half hour fluxes by multiplying the averages by 1800 s (half-hour)\({}^{-1}\); hourly averages were converted to hourly fluxes by multiply the averages by 3600 s h\({}^{-1}\). Daily GPP was determined as the sum of daily NEE (half-hourly NEE fluxes (hourly for Mead sites)) and daily Re (half-hourly Re fluxes (hourly for Mead sites)):  
\[\text{daily GPP}=\text{daily NEE}-\text{daily Re} \tag{7}\]  
where NEE and Re are positive when the flux is toward the surface. Daily GPP values are in units of g C m\({}^{-2}\) d\({}^{-1}\).  
Field year growing season start dates were determined to be days in which daily GPP values commenced to be greater than zero and field year growing season end dates were determined to be days in which daily GPP values fell below the zero threshold.
# A Novel Ring Spring Vibration Isolator for Metro Superstructure
## 3 Results
### Simulation Results
#### 3.1.3 Hysteresis Curve  
To explore the effect of the displacement amplitude on the spring mechanical properties, the ring spring was loaded with a preload displacement of 18 mm at a displacement frequency of 5 Hz with a continuously increasing displacement amplitude (0.5-1-2-3-4 mm). Each amplitude involved five sine loading cycles. It can be seen from the hysteresis curve (Figure 7) that the ring spring has good energy dissipation properties and stable dynamic performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Mechanical** \\ **Parameters** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Normal Capacity Limit \(f\) = 36 mm** \\ **Bearing** \\ **Capacity \(F\) (kN)** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Normal Capacity Limit \(f\) = 48 mm** \\ **Bearing** \\ **Uninstall** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Stiffness \(K\) (kN/mm)** \\ **Capacity \(F\) (kN)** \\ \end{tabular} } \\ \hline \begin{tabular}{c} Numerical \\ simulation \\ \end{tabular} & 658.268 & 18.284 & 8.117 & — & 18.284 & 8.105 \\ \hline \
\begin{tabular}{c} Theoretical \\ calculation \\ \end{tabular} & 665.227 & 18.479 & 8.226 & 886.969 & 18.479 & 8.226 \\ \hline
\begin{tabular}{c} Aberration \\ \end{tabular} & 1\% & 1\% & 1.3\% & — & 1\% & 1.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of theoretical calculation and numerical simulation.  
Figure 6: Comparison of load–displacement curves between theoretical calculation and numerical simulation for different capacity limits: (**a**) 36 mm; (**b**) 48 mm.  
Since the preload displacement of the ring spring is 18 mm, the area enclosed by the hysteresis curve is very small when the displacement amplitude is 0-0.7 mm (Figure 8a). Additionally, the load-displacement curve of the spring is straight, with a slope of approximately 145 kN/mm. When the displacement amplitude is greater than 0.7 mm, the hysteresis curve of the spring is a rhombus (Figure 8b), which becomes fuller as the displacement increases. The energy dissipation capacity of the ring spring also increases as the displacement amplitude increases.
# Advanced Sensor Integration and AI Architectures for Next-Generation Traffic Navigation
###### Abstract  
Traffic congestion represents an urban challenge that authorities are trying to solve through various means. Current traffic management systems do not solve these challenges, which is why the research presents a new proposal for a traffic optimization system. The proposed solution integrates small-sized equipment (ESP32 equipped with accelerometers, gyroscopes, and cameras), cloud-based AI services (Azure Content Safety), and a multi-parametric analytical framework for real-time navigation. The system uses the Traffic Optimization Algorithm (TOA) proposed by the authors to calculate the Global Route Quality Indicator (GRQIK). It associates each route with a value based on which the degree of optimality is estimated. GRQIK is calculated based on the distance traveled, traffic delays, estimated travel time, road safety, and the individual's sensitivity. Real-time data are collected using ESP32, with a pothole detection threshold set at 0.8 rad/s. Through the TomTom API, four alternative routes are identified. The performance evaluation showed that GRQIK differentiates route quality, with scores ranging from 26.40% for optimal routes to 100% for the least favorable ones. In addition, Azure's Content Safety API achieved 100% accuracy in identifying violent incidents and accidents. The limitations of the research concern the small number of images available to test the Content Safety service. The research establishes new approaches for future developments in the field of smart transportation.  
artificial intelligence; traffic optimization; intelligent transportation systems; IoT sensors; real-time navigation; Azure Content Safety; urban mobility Article