esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Management of Electrical Grids by Means of WSANs  
The transformation suffered by the classical electrical network due to the installation of MGs (mainly the application of active management techniques at the distribution network level) allows the application of new technologies in terms of sensors and actuators, and the wireless communication protocols to transmit the measured data and the messages containing the control instructions. This section introduces first the main characteristics of a Wireless Sensor and Actuator Network, to survey afterwards the potential of WSANs for the intelligent management of electrical grids, from the power generation systems to the end user, passing through energy transmission and distribution networks where MGs are usually placed.
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 5 Future Work: A Physical Model, Validated with Measurements  
The creation and validation of the physical model is part of ongoing research that will be published in a separate paper. However, the design principles will be detailed here to encourage others to look along similar lines and improve on it.  
The physical model could may its input from the building density, the density of devices per building, the emitted effective isotropic radiated power (EIRP) of each device, the distance from the buildings to the sensor, the propagation paths on the frequency of observation. There will be multiple propagation paths from source to sensor possible: direct coupling in the Near Field, Line-of-Sight, ground wave, sky wave. These propagation paths were described in [13; 14] and can be seen as parallel paths to the sensor, see Figure 10. Thismodel will be supported mathematically, using existing models for radio wave propagation and wall attenuation.  
Which path is dominant for each source will depend on distance and frequency. To obtain the noise level, the number of buildings within the Radius of Significance (RoS), the radius around the sensor beyond which the contribution of more noise sources will become negligible, must be known. This is depicted in Figure 11. The number of buildings may be an educated guess, or it can be obtained using a Geographical Information System (GIS). The dominant propagation mechanism determines the RoS, and therefore this distance is frequency dependent. While not named as such, a first hint towards the concept of the RoS can already be found in the thesis of [PERSON] [19], who showed a correlation between the number of houses in a circle of a certain radius and the measured man-made noise level. [PERSON], [PERSON], [PERSON] and [PERSON], on the other hand, modelled the distribution of urban man-made noise levels across an area [20].  
Figure 10: A physical model of radio noise arriving at the sensor.  
To evaluate model performance, a large number of measurement location coordinates may be randomly selected from the Radio Noise Data Bank (RNDB) and compared with model output for the same location. The root-mean-square error (RMSE) of the difference may serve as a figure-of-merit. Further test on random points of selective subsets of the RNDB data may be necessary, to prevent that a low overall RMSE is achieved, while performance is poor in in specific regions of the parameters space.
# Detecting Axial Ratio of Microwave Field with High Resolution Using NV Centers in Diamond
## 4 Conclusions  
In summary, we proposed a new method that can detect the axial ratio of microwave fields with high resolution for wide-field and a short time scale. The measurement method relied on detection of two circularly-polarized MW magnetic fields that rotate in different directions in the plane perpendicular to the NV axis. The axial ratio detecting of the MW magnetic field was firstly demonstrated using a single NV center in diamond, achieving a sensitivity of \(1.7~{}\upmu\mathrm{T}/\sqrt{\mathrm{Hz}}\). Then, wide-field detection of the MW magnetic field with an imaging field size of \(85\times 85~{}\upmu\mathrm{m}^{2}\) was displayed. The single NV center in the type-IIa CVD diamond investigated was generated by ion implantation, and the ensemble NV centers in the general CVD diamond were inherent. In a future study, we will apply this technique to test the polarization-controllable MW devices that we designed for selective manipulation of NV sublevels.The method we proposed can effectively benefit the design of MW fields with special polarization properties at the sub-millimeter to millimeter scale and thus benefit the development of techniques relying on quantum spin manipulation [28; 29; 30] and a small-sized antenna.  
Conceptualization, C.-H.L.; methodology, C.-H.L.; software, Y.Z.; validation, D.-F.L., A.M.D.; formal analysis, C.-H.L.; investigation, C.-H.L.; resources, F.-W.S.; data curation, D.-F.L.; writing--original draft preparation, C.-H.L.; writing--review and editing, C.-H.L., A.M.D.; visualization, C.-H.L.; supervision, F.-W.S., A.M.D.; project administration, Y.-S.G.; funding acquisition, Y.-S.G., A.M.D. This research was funded by \"Development of High Sensitivity Diamond NV Magnetic Sensor\" in Chinese Academy of Sciences Research Equipment Development Project, the Strategic Priority Program in Space  
Figure 6: (**a**) ODMRspectra of Spot A in Figure 5b extracted with four different MW sources’ power fed in. The line width and dip contrast increase with the MW source power. (**b**) ODMR signal contrast (red square) and FWHM (blue dot) versus MW source power extracted from ODMR spectra of Spot A with different MW source powers fed in.  
Science of CAS grant number XDA1502030105, the Strategic Priority Research Program of the Chinese Academy of Sciences grant number XDA14040204, the National Key Research and Development Program of China grant numbers 2016 YFB0501300 and 2016 YFB0501304 and the APC was funded by \"Development of High Sensitivity Diamond NV Magnetic Sensor\" in Chinese Academy of Sciences Research Equipment Development Project.  
The authors declare no conflict of interest.
# Numerical Investigation on Novel Shear Connectors in Prefabricated Composite Beams
###### Abstract  
An innovative horizontal-arranged shear stud was proposed for prefabricated composite beams, in response to the engineering demand for accelerated precast decks construction on steel-concrete composite bridges. In order to verify the shear behavior of the studs, a push-out test was performed in which two groups of specimens were designed, and each group included three specimens. In addition, a finite element method based on the ABAQUS2020 software was adopted to analyze the influence factors of the prefabricated studs, which reveals the most influential factor, to provide a guide for optimum design. Therefore, 10 models were established, including one finite element model with the same parameters as the push-out test, and 9 models based on an orthogonal testing design. The results showed that, firstly, the prefabricated shear studs had longitudinal shear resistance, the same as conventional shear studs. Secondly, the ultimate load capacity, as well as the ductility of the prefabricated studs, was better than conventional studs. Thirdly, the modeling results were in good agreement with the push-out test results, which indicated the validity of the modeling method. Lastly, by analyzing the calculated results of the nine orthogonal models, it was found that the stud diameter was the most influential factor for the shear capacity, and the shear capacity was directly proportional to the stud diameter, while it was not proportional to the other factors. This study is evidence that the orthogonal testing design has a high efficiency in finite element modeling, which will provide a guide for the optimum design of prefabricated shear studs.  
prefabricated composite beam; shear studs; push-out test; orthogonal testing design; FEM; shear behavior +
Footnote †: journal: Applied Sciences  
1
## 2 System Principle
### Pseudorange Observation Equation for Receivers in Tunnels
\begin{cases}\rho_{BDS}^{i}=r_{BDS}^{i}+\text{C}\cdot\left(\delta tr_{BDS}- \delta t_{BDS}^{i}\right)+\text{C}\cdot t_{BDS}+I_{BDS}^{i}+S_{BDS}+\text{ \epsilon}_{BDS}^{i}&\text{(a)}\\ \rho_{GPS}^{i}=r_{GPS}^{i}+\text{C}\cdot(\left(\delta tr_{BDS}+\tau_{1}\right)- \left(\left(1-A_{1 GPS}\right)\cdot\delta t_{BDS}^{i}-A_{0 GPS}\right)\right)+ \text{C}\cdot t_{GPS}+I_{GPS}^{i}+T_{GPS}^{i}+S_{GPS}+\text{\epsilon}_{GPS}^{i}& \text{(b)}\\ \rho_{GLO}^{i}=r_{GLO}^{i}+\text{C}\cdot(\left(\delta tr_{BDS}+\tau_{2}\right)- \left(\left(1-A_{1 GLO}\right)\cdot\delta t_{BDS}^{i}-A_{0 GLO}\right))+\text{C} \cdot t_{GLO}+I_{GLO}^{i}+T_{GLO}^{i}+S_{GLO}+\text{\epsilon}_{GLO}^{i}&\text{(c )}\end{cases}  \tag{8}\]  
GNSS signals propagate through air at the speed of light, C, while the propagation speed V in LCX cables is significantly lower. GNSS receivers calculate pseudorange measurements by multiplying the signal propagation time by the propagation speed, assuming this speed to be C, as if the signal were traveling entirely through air. Due to the difference in propagation speeds between LCX cables and air, the signal takes longer to travel the same distance in LCX cables than in air. This discrepancy introduces corresponding errors in the pseudorange measurements obtained by GNSS receivers, as illustrated in Figure 7.  
The error between the GNSS receiver's observed distance D for signal propagation in the LCX and the actual measured distance R is given by Equation (9):  
\[\Delta dis=D-R=\frac{R}{V}\cdot C-R=\left(\frac{C}{V}-1\right)\cdot R \tag{9}\]  
The propagation distance of GNSS signals in the LCX cable is L, and the pseudorange observation of this distance by the receiver is L\({}_{air}\). The velocity error correction model (VEM) for the observation error of the GNSS signal propagation distance in the LCX cable is given by Equation (10):  
\[L_{air}=\frac{C}{V}\cdot L \tag{10}\]
## 4 Numerical Simulations and Discussion
### Comparison with Other AOA Methods  
In this section, the performance of the PDDA method is compared with four commonly used AOA methods namely: MVDR, MUSIC, Min Norm, ESPRIT; and also with two algorithms which have been proposed recently, namely OGBSI and Root-SBL. The simulation codes of some methods are provided by MATLAB R2016b, for example the MUSIC algorithm and the Capon algorithm [43]. Four scenarios are considered in this comparison namely: number of snapshots taken, signal to noise ratio (SNR), the correlation between sources of the incident signals and the execution time. For the first three scenarios, the average root mean square error (ARMSE) is computed for each criterion. The ARMSE can be defined as follows:  
\[\text{ARMSE}=\frac{1}{K}\sum_{j=1}^{K}\sqrt{\frac{1}{D}\sum_{k=1}^{D}\left[ \left(\theta_{k}-\hat{\theta}_{k}\right)^{2}\right]} \tag{25}\]  
where \(D\) is the number of signals arriving, \(\theta_{k}\) is the actual angle, \(\hat{\theta}_{k}\) is the estimated angle and \(K\) is the number of Monto Carlo simulation trials. For simplicity in calculating each RMSE, we proceed as follows. We have \(D\) actual DOAs and L estimated DOAs. In the practical applications or even in the simulation scenarios, the number of detected peaks can be fewer or more than actual DOAs. Three scenarios are possible namely: \(D\) = L, \(D\) < L, \(D\) > L. In the case of L > \(D\) (because of false peaks), we include only the estimates for the \(D\) loudest peaks. Then, in all cases, we calculate the L \(\times\)\(D\) array of  
Figure 6: Performance estimation of PDDA method with three AOAs close together. (**a**) 3D plot of the PDDA method; (**b**) 2D plot of the PDDA method.  
angle error magnitudes between each pair of actual and estimated DOAs and then take the minimum for each of \(D\) columns and average the root mean square values.
## 6 Conclusions  
In this paper, two source enumeration approaches named AREG and T-GANE are proposed. Both approaches employ gaps of eigenvalues from the covariance matrix of the received signals along multiple antenna arrays; AREG uses the ratio of the NS gap to the mean of accumulated NN gaps, while T-GANE uses the gaps of the normalized eigenvalues to compute the threshold by machine learning based clustering approaches. The criterion formula of AREG using the gaps of eigenvalues is derived, and a source enumeration criterion with AREG is presented. Three steps of the T-GANE procedure are also described: dataset generation, learning and computing optimal threshold, and source enumeration using the optimal threshold. The simulation results show that AREG provides better accuracy of source enumeration than that of MDL and SORTE at a low SNR range and is also better than that of AIC at high SNR. It is also shown that T-GANE with appropriate learning datasets outperforms both AIC and MDL in high and low SNR. This feasibility shows that the appropriate parameter settings for generating learning datasets of T-GANE in the designated SNR range are sought to improve the T-GANE as future research work.  
Figure 10: The estimation accuracy of AIC, MDL, and T-GANE versus SNR. T-GANE (\(-20\) to \(10\)) and T-GANE (\(-14\) to \(-11\)) denote that the learning datasets for T-GANE are generated for SNR range \(-20\) dB to \(10\) dB and \(-14\) dB to \(-11\) dB, respectively. The number of signal sources \(D\) is randomly selected from a set \(\{1,2,\ldots,6\}\) at each trial.  
**Author Contributions:** Conceptualization, Y.L., C.P., Y.C., and K.K.; methodology, Y.L. and C.P.; software, Y.L., C.P., and T.K.; validation, Y.L. and C.P.; formal analysis, Y.L. and C.P.; investigation, Y.L., C.P., and T.K.; resources, Y.C. and K.K.; data curation, Y.L., D.K., M.-S.L., and D.L.; writing--original draft preparation, Y.L., C.P., and T.K.; writing--review and editing, Y.L., Y.C., and K.K.; visualization, Y.L., C.P., and T.K.; supervision, Y.C., K.K., D.K., M.-S.L., and D.L.; project administration, Y.C., K.K., D.K., M.-S.L., and D.L.; funding acquisition, Y.C., K.K., D.K., and D.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Agency for Defense Development, Republic of Korea, under the contract \"V/UHF Communication Electronic Support System of Electronic Warfare UAV\".  
**Institutional Review Board Statement:** Not applicable.  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
### Limitations  
The spectral parameters namely LFB, MPF, H/L-FB, DSI, and H/L-SM were extracted from the sEMG signal which, however, was non-stationary during the test, and wasaffected by many confounding factors [41], including electrode location, thickness of the subcutaneous tissues, the detection system used to obtain the recording, changes in the transmembrane action potential, and cross-talk from nearby muscles.  
Due to all these confounding factors, caution is needed when using changes in the sEMG parameter as a predictor of the level of muscle fatigue. Although these factors could not be entirely excluded and do affect the estimated spectral parameters, we followed the recommendations that the careful placement of the electrodes between the innervation zone and the tendon and the normalized amplitudes should minimize the influence on the results.  
The logarithmic transformation was used to convert the sEMG spectral parameters during the endurance dynamic contractions to minimize the effects of these factors [42]. [PERSON] et al. (2001) demonstrated that muscle fatigue could be assessed during dynamic contractions using a short-term Fourier transformation [43]. Some authors extracted the signal spectral moments as spectral parameters using Fourier transformation [44], [PERSON] et al. (2008) found that continuous wavelet transformation and traditional Fourier transformation are generally reliable to assess muscle fatigue [45].  
As mentioned in the introduction, the endurance capacity is problematic in measuring the effect of physical and psychological factors such as pain and motivation. Because many factors can influence sEMG spectral parameters, the correlation of their changes with endurance might be lower than expected.
## 2 Preliminaries  
Graph \(G(V,E)\) is a collection, which is composed of a vertex set \(V(G)\) and edge set \(E(G)\). In this paper, the element of \(E(G)\) denotes fracture.  
Definition 1: [12] Make \(G=(V,E,W)\) be a weighted graph, where \(V\) is the vertex set, \(E\) is the edge set, and \(W\) is the weight matrix for \(E\). \(W\) represents the cost from one vertex \(u\) to other vertex \(v\) (indicated by \(\omega(u,v)\)). If \(e=(u,v)\), replace \(\omega(u,v)\) with \(\omega(e)\).  
Definition 2: [12] The length of a path \(p=(e_{0},e_{1},...,e_{k})\) is the sum of the weights of its constituent edges:  
\[length(P)=\sum_{i=1}^{k}\omega(e_{i-1},e_{i}). \tag{1}\]  
Definition 3: [12] The distance from \(u\) to \(v\), shown as \(\delta(u,v)\), is the shortest path with the minimum sum of the weights on the edges in a \(u-v\) path. \(u,v\) denoted start vertex and sink vertex, respectively.  
The following example is given to illustrate the meaning of **Definition 1-3**, which is shown in Figure 2. Vertex \(A\) and \(D\) are the start vertex and terminal node, respectively. The weight value and shortest path can be obtained:  
\[V=\{A,B,C,D\},\]  
\[E=\{\omega(A,B)=3,\omega(A,D)=9,\omega(A,C)=5,\omega(B,D)=2,\omega(C,D)=1\}.\]  
From **Definition 2**, it is easy to obtain that \(length(A,D)=\omega(A,D)=9\) or \(length(A,D)=\omega(A,B)+\omega(B,D)=5\). Hence, the distance from \(A\) to \(D\) is 5.
## 2 Materials and Methods
### DInSAR Technique
The system (1) is a simplification of the complete system that is composed by a number of unknown elements (\(V_{EW}\), \(V_{NS}\), and \(V_{z}\)) greater than the number of Equation (2), taking into account also the \(NS\) component. Equation (1) should be applied to pairs of spatially coincident \(ASC\) and \(DES\) points. The spatial coincidence of \(ASC\) and \(DES\) PSs is almost impossible, but the composition can be also performed using pairs of points that can be considered representative of the same reflective target, contemplating a very limited positioning error. For practical purposes, the interferometric products are usually returned as structured ASCII files that can be easily managed in a code language such as _python_. The satellite data used in this work, acquired through the standard Stripmap mode, derive from the Italian COSMO-SkyMed (CSK) X-band satellite constellation. For the CSK Stripmap mode data, on average, the portion of surface has a full spatial resolution of about 3 m \(\times\) 3 m. For a CSK dataset composed by over 100 images, processed at full spatial resolution and with a mean look angle of about 33\({}^{\circ}\), the precision values corresponding to one standard deviation (1\(\sigma\)) are the following: about 1-2 m, 2-3 m, and 1-2 m in the N-S, in the E-W and in the vertical directions, respectively.
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## 1 Introduction
Since the order of the responses is determined in advance, we only need to calculate the TDOA based on the time-stamps of the received peaks. For example, when all anchors send messages (quasi-)simultaneously, an (unlimited) number of agents can use TDOA to estimate their positions. SAL is one of the current development trends due to its low infrastructure requirement. Localizing agents using a single anchor are usually assisted by MPCs [37] or an antenna array [38] based on time-of-arrival (TOA) and AOA measurements. In particular, the antenna array can be used to obtain high-accuracy angle estimation based on phase-difference-of-arrival (PDOA) of the signals [39; 40; 41; 42], which translates to high-accuracy localization of the agents. The UWB based SAL system is developed in [43], which can achieve decimeter-level localization accuracy with a three-element array. Moreover, cooperation among agents can enable network localization by using a passive single anchor [44]. However, time efficient measurement protocols for SAL have not been well investigated, and such protocols are essential for multi-agent localization with low latency and minimum infrastructure. In this paper, we propose an efficient SAL method using UWB systems, where the anchor is equipped with an antenna array for both range and angle estimations. In this method, the anchor initializes the measurement process by broadcasting the Init and Sync messages, and then all agents send the Resp messages (quasi-)simultaneously according to a preset order. The anchor can estimate the agents' TOAs and AOAs from a single CIR, thereby realizing multi-agent localization. Finally, we implement the system on low-cost hardware, and the experimental results shows that our method can achieve decimeter-level localization accuracy. The main contributions of this paper are as follows. * We propose a high-efficiency SAL method which can determine the positions of multiple agents within minimum transmissions of signals. * We determine the time-stamps and phases of all the received peaks from different agents in the CIR, which enables range and angle estimations of multiple agents. * We implement the system on a low-cost platform in two indoor environments, in which decimeter-level localization accuracy is achieved. The rest of this paper is organized as follows. Section 2 describes the signal model of our system. Section 3 details the communication protocols and advantages of the proposed SAL method. Section 4 describes the TOA and AOA estimation method and the localization algorithm. Experiments and result analysis are shown in Section 5. The conclusions are drawn in Section 6.
###### Abstract  
Designing integrating systems for support, real-time monitoring, and executing of complex missions is challenging, since they often fail due to high levels of complexity and overwhelming volume of input data. Past attempts have resorted to \"ad hoc\" solutions, which face issues of being non-updatable, non-upgradable, and not applicable to similar missions, necessitating a complete redesign and reconstruction of the system. In the national defense and security sector, the impact of this reconstruction requirement leads to significant costs and delays. This study presents advanced methodologies for organizing large-scale datasets and handling complex operational procedures systematically, enhancing the capabilities of Decision Support Systems (DSSs). By introducing Complex Mission Support Systems (CMSSs), a novel SS sub-component, improved accuracy and effectiveness are achieved. The CMS includes mission conceptualization, analysis, real-time monitoring, control dynamics, execution strategies, and simulations. These methods significantly aid engineers in developing DSSs that are highly user-friendly and operational, thanks to human-reasoning-centered design, increasing performance and efficiency. In summary, the systematic development of data cores that support complex processes creates an adaptable and adjustable framework in a wide range of diverse missions. This approach significantly enhances the overall sustainability and robustness of an integrated system.  
large-scale information; big data; complex missions; decision support system; operational procedures; support systems; decision-making process; information system; sustainability +
Footnote †: journal: Applied  
Article