esa-sceva/satcom-chunk-collection huggingface dataset:
applied sciences Article  
A Practical Harmonic Admittance Matrix Derivation Approach for Fluctuating Power Photovoltaic Systems [PERSON]  
C College of Automation, Nanjing University of Posts and Telecommunications, Wenyuan Road 9, Nanjing 210023, China; [EMAIL_ADDRESS] (X.X.); [EMAIL_ADDRESS] (A.D.);  
[EMAIL_ADDRESS] (Q.L.)  
* Correspondence: [EMAIL_ADDRESS]  
Due to a range of economic incentives and policy supports, distributed photovoltaic (PV) systems are accelerating their penetration into the distribution network at all voltage levels. However, the PV systems are connected to the grid via power electronic converters, which are nonlinear devices characterized by inherent harmonic emission, and their cumulative harmonic injection into the grid is detrimental to the grid power quality. Although the existing literature proves that harmonic admittance matrix (HAM)-based models can represent well the supply voltage dependence of harmonics, the conventional HAM derivation approach is based on the harmonic sensitivity tests conducted under laboratory conditions, making it infeasible for infield implementation. To address this issue, this paper starts with investigating the harmonic emission and grid interaction mechanisms of PV systems analytically, followed by analyzing the power dependency of HAMs experimentally. Based on the findings, a HAM derivation and self-tuning approach is proposed for fluctuating power PV systems, where only the infield measurements at the point of connection are needed. The model accuracy is compared against the widely used constant current source model and harmonic Norton model, while its integration approach for harmonic power flow analysis is demonstrated via the simulated European low voltage test feeder.  
harmonics; harmonic modelling; photovoltaic systems; power dependency; harmonic admittance matrix
# Dual-Band Wearable Antenna Integrated with Glasses for 5G and Wi-Fi Systems
## 1 Introduction
Its low-loss glass substrate and embedded feeding structure provide a cost-effective, high-performance solution for advanced communication in a wearable form. Table 1 presents the bands and technologies of glass-integrated antennas reported in the literature. This paper addresses a critical research gap: the development of a low-profile antenna capable of dual-band operation at 3.5 GHz and 5.8 GHz, specifically designed for seamless integration into eyeglasses. Our design approach centers on creating an antenna that can be incorporated into the existing structure of standard glasses without necessitating \begin{table}
\begin{tabular}{c c c} \hline \hline
**Publication** & **Bands [GHz]** & **Technology** \\ \hline
[12] & 2.4 & Monopole fabricated with physical vapor \\  & & deposition on the glass surface \\
[13] & 2.4 & A slot loop antenna formed by a \\
[14] & 2.4 & metal-mesh-based transparent antenna film \\
[15] & 0.8, 1.7, 3.3, 4.8 & Multi-port antenna, arbitrarily shaped loops \\  & & around lenses, slots \\
[16] & 2.4, 5.8 & Inverted T on a dielectric substrate with \\  & & 2-layer metallization \\
[17] & 2.4, 5 & Monopole on FR4 substrate \\
[18] & 4.5, 6.4 & Inverted F antenna radiates through the slot \\
[19] & 76 & On-chip microstrip antenna array \\ This & & **3.5, 5.8** & Modified inverted F antenna \\ research & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The bands and technologies of glass-integrated antennas reported in the literature. fundamental alterations to their construction. Given that the intended application involves a wireless camera system positioned at the front of the eyeglasses, the antenna must be specifically contoured to fit discreetly within the temple of the glasses. This particular integration challenge forms the core of the research presented in this paper. Our objective is to contribute a novel antenna design that not only meets these stringent dimensional and operational requirements but also ensures efficient and reliable data transmission for advanced assistive devices aimed at the visually impaired.
## 4. Scope and Applicability
### Critical Infrastructures for Emergency Management  
The management of public events and crowd control is a typical example of mission critical services; a service downtime could undermine citizen security and create problems in the service continuity leading to very serious issues. This type of system collects data from people using in-place nodes, e.g., Wi-Fi/Bluetooth. With this information the system is able to estimate the number of people in a specific area and, taking advantage of the bandwidth and low latency features offered by 5G connectivity, send these data to the edge nodes so that they are able to determine if a critical condition is going to happen, for example, if the flow of people is coherent with the data collected on the buses or in other public transportation systems. In this situation, if an edge node goes down, fault-tolerance mechanisms need to be put in place. However, while in many other cases it is possible to use fault-tolerance features of many of the most common orchestrators (such as Kubernetes) this is not possible for stateful services. In this respect, the CloudOps self-healing approach, including automatic data portability techniques, can provide support to the Operators of the system. Similarly, instead of managing the service continuity by having two edge nodes, it could be possible to move computation across the continuum moving from the edge to the cloud. In both cases, using stateful objects, the self-healing mechanisms will ensure that in the event of a fault, operations continue without interruption by providing data on the movement of the crowd.
# Wind-Induced Dynamic Performance Evaluation of Tall Buildings Considering Future Wind Climate
## 2 Basic Settings for the Simulations
### Performance Evaluation of the Building Under Wind Loads
#### 2.2.1 Generation of Wind Speed Time History
The power spectral density matrix \(\mathbf{S}(\omega)\) is decomposed by the Cholesky factorization to obtain \(\mathbf{H}(\omega)\), and the fluctuating wind velocity time history at the \(j\)th level can be generated with weighted amplitude harmonic wave superposition. The Cholesky decomposition method is carried out for the decomposition of the PSD matrix \(\mathbf{S}(\omega)\) as follows:  
\[\mathbf{S}(\omega)=\mathbf{H}(\omega)^{T*}\mathbf{H}(\omega) \tag{10}\]  
where \(\mathbf{H}(\omega)\) is a lower triangular matrix as depicted in Equation (11), whose diagonal elements are real and non-negative functions of natural frequency (\(\omega\)). The superscript \(T\) indicates the matrix transpose, while the asterisk represents the complex conjugate. \[\mathbf{H}(\omega)= \begin{pmatrix}H_{11}(\omega)&\dots&0\\ \vdots&\ddots&\vdots\\ H_{n1}(\omega)&\dots&H_{m}(\omega)\end{pmatrix}  \tag{11}\]The wind turbulence time history, denoted as \(u_{j}\)(t) at the \(j\)th story level, is computed by the trigonometric function's superposition with random phase angles, as formulated by Equation (12):  
\[u_{j}(t)=2\sum_{q=1}^{n}\sum_{l=1}^{N_{\omega}}\Bigl{|}H_{jq}\Bigl{(}\omega_{ql }\Bigr{)}\Bigl{|}\sqrt{\Delta\omega}\,cos\Bigl{[}\omega_{ql}t-\vartheta_{ql} \Bigl{(}\omega_{ql}\Bigr{)}+\phi_{ql}\Bigr{]} \tag{12}\]  
where \(\Delta\omega\) is the frequency interval, defined as \(\omega_{u}/N_{\omega}\), with \(\omega_{u}\) being the cut-off frequency (set at 2 rad/s) and \(N_{\omega}\) the number of divisions in the frequency domain (set to 500). The frequency \(\omega_{ql}\) is computed by Equation (13):  
\[\omega_{ql}=l\Delta\omega-\frac{q-1}{q}\Delta\omega \tag{13}\]  
The vector \(\Phi_{ql}\) consists of random numbers drawn from a uniform distribution between \([0,2\pi]\), and \(\theta_{ql}\) is given by Equation (14):  
\[\vartheta_{ql}(\omega)=tan^{-1}\left\{\frac{Im[H_{jq}\Bigl{(}\omega_{ql} \Bigr{)}]}{Re[H_{jq}\Bigl{(}\omega_{ql}\Bigr{)}]}\right\} \tag{14}\]  
where Im[H] and Re[H] indicate imaginary and real quantities of H, respectively. The time-varying wind speeds at each representative floor of the building were simulated by summing up the mean wind (from Equation (2)) and turbulence component (from Equation (12)) as per Equation (1). Figure 5 compares the simulated historical, mid-future, and late-future wind speed time histories at the 44 th floor of the building for 10-year, 50-year, and 100-year return period wind speed.
# Optimal Power Dispatch of an Offshore Wind Farm under Generator Fault
## 5 Case Study
### Case 2: Comparison of Power Fluctuation  
In Case 2, the wind speed was set to 12 m/s. \(P_{\text{dem}}\) was 17 MW in the first state and changed to 16 MW in the next state. To illustrate the effect of the power fluctuation term in the objective function, the WF operated twice in ODS. For the first time, \(k2\) was set to zero, which means that the power fluctuation was not taken into account. For the second time, \(k2\) was set to four. Then, the results of these two time simulations were compared. The result is shown in Table 3 and Figure 12.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**PDS**} & \multicolumn{2}{c}{**ODS**} \\ \cline{2-5}  & \(P_{\text{r}}\) **(MW)** & \(P\) **(MW)** & \(P_{\text{r}}\) **(MW)** & \(P\) **(MW)** \\ \hline WT1 & 5.00 & 5.00 & 5.00 & 5.00 \\ WT2 & 4.20 & 4.17 & 4.17 & 4.17 \\ WT3 & 3.13 & 3.11 & 3.01 & 3.01 \\ WT4 & 2.86 & 2.84 & 3.00 & 3.00 \\ WT5 & 2.78 & 2.76 & 2.81 & 2.81 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of power generation capacity in Case 1. PDS, Proportional Dispatch Strategy.  
Figure 11: Comparison of power generation capacity in Case 1: (**a**) power reference and power output in PDS; (**b**) power reference and power output in ODS.  
When \(k2=0\) and the power demand is smaller than the available power of the WF, many different power reference combinations can be used as the final result. Because the PSO algorithm itself is a random process, this may result in a large change in the power reference of the WT between two states. The large change of power reference of WT5 in PDS is a good illustration to explain the impact of the random process in PSO on power fluctuation.  
When \(k2=4\), power fluctuation is one of the optimization objectives. The power reference of the next state will be as close as possible to that of the previous state. The correlation coefficient of the power references between the two states can be used to evaluate the degree of the power change. The larger the correlation coefficient, the smaller the power fluctuation. The correlation coefficient was improved from 0.8161 to 0.9987.
## 2 Theoretical Analysis of Moon-Based Earth Observational Angles
### Definition and Calculation of Observational Angles
#### 2.2.2 Definition and Calculation of Observational Angles
The viewing zenith angle \(\delta_{m}\) can be expressed as follows:  
\[cos(\delta_{m})=\frac{cos(\beta_{m})-\frac{R}{r_{m}}}{\sqrt{1+\big{(}\frac{R} {r_{m}}\big{)}^{2}-2\times\frac{R}{r_{m}}\times cos(\beta_{m})}}, \tag{21}\]  
where  
\[cos(\beta_{m})=sin(\theta_{m})sin\big{(}\theta_{p}\big{)}cos\big{(}\varphi_{m }-\varphi_{p}\big{)}+cos(\theta_{m})cos\big{(}\theta_{p}\big{)}. \tag{22}\]  
Similarly, by replacing \(\theta_{m}\) and \(\varphi_{m}\) in Equations (25) and (26) with \(\theta_{p}\) and \(\varphi_{p}\), the solar zenith angle can be calculated:  
\[cos(\delta_{s})=\frac{cos(\beta_{s})-\frac{R}{r_{s}}}{\sqrt{1+\big{(}\frac{R} {r_{s}}\big{)}^{2}-2\times\frac{R}{r_{s}}\times cos(\beta_{s})}}, \tag{23}\]  
where  
\[cos(\beta_{s})=sin(\theta_{s})sin\big{(}\theta_{p}\big{)}cos\big{(}\varphi_{ s}-\varphi_{p}\big{)}+cos(\theta_{s})cos\big{(}\theta_{p}\big{)}. \tag{24}\]  
The relative azimuth angle \(\Delta\varphi\) refers to the difference between the solar and lunar azimuth angles and can be calculated as follows:  
\[cos(\Delta\varphi)=\frac{\overset{\rightarrow}{S\cdot\overset{\rightarrow}{M} }}{\big{\|}\overset{\rightarrow}{S}\big{\|}\overset{\rightarrow}{M}\big{\|}}, \tag{25}\]  
where  
\[\overset{\rightarrow}{S}=\overset{\rightarrow}{PS}-\frac{\overset{ \rightarrow}{PS\cdot\overset{\rightarrow}{P}}}{\big{\|}\overset{\rightarrow }{P}\big{\|}^{2}}\overset{\rightarrow}{P}=r_{s}\left[ \begin{array}{c} sin\big{(}\theta_{p}\big{)}cos(\theta_{s})-cos\big{(}\theta_{p}\big{)}sin(\theta_{s}) cos\big{(}\varphi_{s}-\varphi_{p}\big{)}\\ sin(\theta_{s})sin\big{(}\varphi_{s}-\varphi_{p}\big{)}\end{array} \right] \tag{26}\]  
\[\overset{\rightarrow}{M}=\overset{\rightarrow}{PM}-\frac{\overset{ \rightarrow}{PM}\cdot\overset{\rightarrow}{P}}{\big{\|}\overset{\rightarrow }{P}\big{\|}^{2}}\overset{\rightarrow}{P}=r_{m}\left[ \begin{array}{c}sin \big{(}\theta_{p}\big{)}cos(\theta_{m})-cos\big{(}\theta_{p}\big{)}sin( \theta_{m})cos\big{(}\varphi_{m}-\varphi_{p}\big{)}\\ sin(\theta_{m})sin\big{(}\varphi_{m}-\varphi_{p}\big{)}\end{array} \right] \tag{27}\]  
\(\overset{\rightarrow}{S}\) and \(\overset{\rightarrow}{M}\) are the two-dimensional vectors projected on the XY plane of the line-of-sight vector from the sensor and Sun, respectively. In summary, the observational angles are mainly determined based on the relative positions of the observed point and lunar and solar nadirs, which are affected by the orbital elements of the Moon and Sun.
# Vector Approximate Message Passing Based OFDM-IM Detection for Underwater Acoustic Communications
## 2 System Model
To realize reliable data detection, the signal for the \(g\)th group \(\mathbf{y}^{(g)}\) in \(\mathbf{Y}\) in the frequency domain is expressed as  
\[\mathbf{y}^{(g)}=\mathbf{H}^{(g)}\mathbf{x}^{(g)}+\mathbf{w}^{(g)},g=1,2, \cdots,G, \tag{10}\]  
where \(\mathbf{H}^{(g)}\in\mathbb{C}^{n\times n}\) is a diagonal matrix with the components from the \(g\)th group vector in \(\mathbf{H}\) and \(\mathbf{w}^{(g)}\) is the \(g\)th group Gaussian noise vector. ML detection is the optimal method and searches all the combinations of indices and constellation points, which is generally defined as  
\[\Big{(}\hat{\mathbf{f}}^{(g)},\hat{s}^{(g)}\Big{)}=\operatorname*{arg\,min}_{ j^{(g)},s^{(g)}}\sum_{\gamma=1}^{k}\Big{|}y_{j^{(g)}_{\gamma}}^{(g)}-h_{j^{(g)}_{ \gamma}}^{(g)}s_{\gamma}^{(g)}\Big{|}^{2}, \tag{11}\]  
where \(y_{\gamma}^{(g)}\) and \(h_{\gamma}^{(g)}\), \(\chi=1,2,\cdots,n\), are the corresponding signal element and channel coefficient of the \(g\)th OFDM-IM subblock. ML detector achieves the optimal error performance. However, it has high complexity which exponentially increases with the size of subblocks and the modulation order. It is impractical for cases with a large number of subcarriers or high-order modulation in UWA communications. Therefore, it is desired to design a low-complexity and effective detector for UWA communications. In the following contents,we propose a novel detector based on VAMP theory by exploiting the inherent sparsity of OFDM-IM symbols in the frequency domain.
# Research on Secure Debugging Interaction of Sensor Nodes Based on Visible Light Communication
## 7 Conclusions  
The insecure debugging interaction may lead to malicious code implantation and continuous sensitive data leakage. In this paper, we attempt to apply visible light communication in debugging interaction of sensor node and improve the security from the physical level. For this, the VLC-based hybrid communication debugging system is designed and realized with COTS devices. The system employs different visible light sensing means in uplink and downlink. For system downlink, due to the ambient light sensor's poor performance, we employ bitmask and ODH-PIM to optimize source and channel coding for improving transmission efficiency. For the system uplink, we also optimize the symbol representation and demodulation procedure of DH-PIM to improve transmission stability of debugging feedback data. Experiments' results demonstrate that HCDS is competent for debugging interaction of the sensor node without any hardware modification.  
In future work, we will dig into the potential capacity of drone for HCDS. Object detection and route recognition techniques will be studied for HCDS with drone. By sending drone to place nearby the lost sensor nodes, it extends VLC-based debugging interaction to most outdoor applications.  
Software, Y.Y.; Writing-original draft, Y.Y.; Writing-review & editing, J.Q., Z.L. and M.C. All authors have read and agreed to the published version of the manuscript.  
This research work is supported by National Key Research and Development Project under grant no. 2018 YFB1402800, Zhejiang Provincial Natural Science Foundation of China under grant no. LY20F020026, and the Research Foundation of Education Department of Hunan Province under grant no. 19K105.  
The authors declare that they have no conflict of interest.  
Figure 11: (a) BER under different distances in uplink. (b) BER under different angles in uplink.
# One-Step Synthesis of Cross-Linked Esterified Starch and Its Properties
## 3 Results and Discussion
### Acid Tolerance and Shearing Tolerance of CES  
The tolerance of CES to shearing was determined (Table 3), and the results indicated the increased shearing tolerance of CES compared to the original waxy maize starch. For the waxy maize starch, the starch molecules will be separated after continuous shearing. In contrast, increasing the interaction between starch molecules by cross-linking contributed to the increased viscosity of CES, making it less sensitive to mechanical shearing. For the samples with different degrees of cross-linking, higher degrees of linking contributed to better tolerance to shearing. In contrast, the degree of esterification had a limited impact on the viscosity of CES. In summary, the shearing tolerance was closely related to the degree of cross-linking, and CLAC-2 and CLAC-5 displayed the highest tolerance to mechanical shearing.  
The acid tolerance of CES was also increased when compared to waxy maize starch. When the pH value was set at 2, the viscosity of CES was not changed, while the results for the waxy maize starch showed the opposite effect. An acidic condition can accelerate the release of starch particles, which then reduced the viscosity of the starch. In contrast,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Content of Acetyl** & \multirow{2}{*}{**(DS)**} & **Content of Conjoined** & \multicolumn{3}{c}{**Sweating Rate (\%)**} \\  & **Acetate (\%)** & & **Phosphate (\%)** & **1 Time** & **3 Times** & **5 Times** \\ \hline Waxy maize starch & 0 & 0 & 0.007 & 45.23 & — & — \\ CLAC-1 & 0.89 & 0.034 & 0.017 & 0.14 & 31.03 & 35.52 \\ CLAC-2 & 0.83 & 0.032 & 0.025 & 24.18 & 28.58 & 43.40 \\ CLAC-3 & 1.02 & 0.039 & 0.022 & 5.56 & 34.03 & 48.52 \\ CLAC-4 & 1.17 & 0.045 & 0.018 & 19.52 & 26.62 & 44.65 \\ CLAC-5 & 0.87 & 0.033 & 0.025 & 5.93 & 29.02 & 34.42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Freeze-thaw stability of waxy native corn starch and cross-linked esterified composite modified starch paste.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Initial Viscosity** & **After Shearing** & \multirow{2}{*}{**Difference after** **Disearing (\%)**} & **Relative** & **After** & **Difference after** & **Relative** \\  & **(mpa-s)** & & **(mpa-s)** & **Shearing (\%)** & **(mpa-s)** & **(mpa-s)** & **(mpa-s)** & **Difference after** \\ \hline Waxy maize starch & 267 & 26 & 241 & 0.90 & 312 & 45 & 16.85 \\ CLAC-1 & 904 & 78 & 826 & 0.91 & 897 & 7 & 0.77 \\ CLAC-2 & 507 & 193 & 314 & 0.62 & 504 & 3 & 0.59 \\ CLAC-3 & 873 & 87 & 786 & 0.90 & 862 & 11 & 1.26 \\ CLAC-4 & 662 & 131 & 531 & 0.80 & 654 & 8 & 1.21 \\ CLAC-5 & 593 & 189 & 404 & 0.68 & 580 & 13 & 2.19 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Shear tolerance and acid tolerance of waxy native corn starch and cross-linked esterified composite modified starch paste.  
the CES exhibited better performance in an acidic condition, which indicated that it had promising applications as a food additive in acidic conditions [22, 23].
## 2 Methodology
### Functional Data Analysis  
The application of the Dragon Transform to the stator current of the induction motor during startup yields a set of time curves. Therefore, Functional Data Analysis tools are the natural choices for analyzing this particular type of data. The application of modern technologies is increasingly allowing us to measure continuous time phenomena as functions. Although the measured curves are often discretized, data sets can be better seen as made of functions rather than finite-dimensional measurements. Functional Data Analysis [19; 20] are the set of statistical tools specially developed to deal with this particular type of data. Many standard statistical tools have been adapted to cope with functional data's specificities, and the classical ANOVA (ANalysis Of VAriance) procedure is one of them. In that spirit, a functional ANOVA test procedure was introduced in [19]. We propose considering this approach to help in the diagnosis of induction motors. This test analyzes whether the mean curves for different conditions can be considered the same or if significant differences among mean curves can be detected. If, as an example, three different conditions (1,2,3) are considered, the functional ANOVA test will have as null hypothesis:  
\[H_{0}:m_{1}=m_{2}=m_{3}, \tag{5}\]  
where \(m_{i}\) is the mean curve for the transformation curves obtained in the \(i\)-th category. The null hypothesis states that \(m_{1}(t)=m_{2}(t)=m_{3}(t)\) for every t in the domain where these functions are simultaneously defined. Then, the functional ANOVA procedure is carried out by applying a kind of asymptotic parametric bootstrap procedure as detailed in [21]. The procedure is implemented by applying the fda.usc package [22] in R [23]. This package is available at the CRAN repository. Other approaches for functional ANOVA can be seen in [24].  
Figure 2: Fault severity quantification of the case shown in Figure 1: time evolution of the energy density along the bar breakage harmonic trajectories (LSH in blue and USH in green).
## Materials and Methods
### Environmental Variables  
The environmental variables used in this study for STN mapping included LULC, remote sensing-derived variables, and topographic and climate variables. These environmental data were collected from various sources and used to generate a total of 15 predictor variables. ArcGIS 10.2 was used to convert all environment variables to raster formats with the same 30-m resolution. These predictor variables and observed STN data were then imported into a geographic information system in a common coordinate reference system for future STN mapping. The pixel values of the predictor variables corresponding to each soil sample point based on these raster layers were calculated to build the models.
## 3 Results and Discussion
### Level of Contribution of the Time Series Features
A comparison of the relevant time series features with the results of the study of [PERSON] et al., [8] shows that for the class TOW, Z-Score VV + VH is also one of the time series features with the highest contribution. For TFV, the most relevant time series characteristic (Z-Score VV - VH) is confirmed only for the study area in the southern area of interest of Greece/Turkey. In the other  
Figure 9: Multitemporal behavior of the backscatter intensity for TFV areas for VV (dB), VH (dB), VV/VH (linear scale), and NDVI values in China. The blue bars mark the analyzed date at the flood event. two example areas, the algorithm determined different relevant time series features due to different environmental conditions (e.g., vegetation type, topography, soil moisture) and their interaction. Based on a previous analysis in [PERSON] et al., [8], the time series feature with the highest contribution can achieve a higher classification accuracy compared to the combination of all time series features. Therefore, a single time series feature with the highest contribution was used for the classification for deriving the desired classes. This identification step is performed automatically by the algorithm using training data. Moreover, these examples show that the use of both polarizations is relevant for the derivation of flooded areas.