esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Analysis Methods
### ERA5, MERRA-2 and CFSv2 Reanalysis Data Sets  
Hourly fields of sea level pressure (SLP), zonal and meridional components of wind speed, geopotential height and temperature were obtained from the ERA5 reanalysis--the latest climate reanalysis produced by the European Center for Medium-Range Weather Forecasts (ECMWF) using the Integrated Forecasting System Cy41r2 with 4D-Var data assimilation. The ERA5 has T639 spectral resolution (corresponds to 31-km horizontal resolution) and provides data at 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) regular latitude-longitude grid for 137 levels from the Earth's surface up to 0.01 hPa [37]. We used the ERA5 data set to track PLs and analyze the atmospheric environment and wind conditions.  
PLs activity was also considered from The National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) operational analysis. The CFSv2 is an advanced coupled atmosphere-ocean-land dynamical climate forecast system [38] that was made operational at NCEP in March 2011. The hourly CFSv2 analysis is based on the improved Climate Data Assimilation System version 2 (CDASv2) with an increased resolution of 27 km (T574) and available with the horizontal grid resolution of about 0.2\({}^{\circ}\)\(\times\) 0.2(for near-surface wind speed) and of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) (for the other parameters). In this study, the term CFSv2 is referred to as the CDASv2 data set.  
We used the Modern Era Retrospective Analysis for Research and Applications (MERRA-2) [39] generated with the Goddard Earth Observing System, Version 5.12.4 (GEOS-5) atmospheric model and Global Statistical Interpolation (GSI) analysis scheme. The MERRA-2 data set has an approximate spatial resolution of 0.5\({}^{\circ}\) latitude by 0.625\({}^{\circ}\) longitude and comprises assimilations of hourly meteorological diagnostic parameters, including wind speed and sea level pressure.  
It is assumed that the satellite estimates of near-surface wind speed in combination with assimilated reanalysis data sets aid to diagnose the influence of mesoscale atmospheric circulations and orographic effects on wind distribution in coastal zones. However, we take into account that reanalysis data sets systematically underestimate high wind speeds in polar lows [21; 40; 41].
## 2 Materials and Methods
### Ground Data Collection
#### 2.2.2 Spectral Reflectance Data  
Winter wheat canopy hyperspectral data were collected using a spectroradiometer (Field Spec 4, wavelength range: 350-2500 nm, spectral resolution: 1 nm) manufactured by ASD (Analytical Spectral Devices Inc., Boulder, CO, USA). The measurements were carried out between 10.00 and 14.00 in good weather conditions and with sufficient illumination. A whiteboard was used for reflectance calibration. The measurement position was approximately 0.5 m from the wheat canopy. At the same position as the _f_PAR measurement, 10 canopy spectral curves were obtained and their average was taken as the spectral reflectance curve of the plot. The near-ground hyperspectral data were then fitted to the reflectance of different bands from the Sentinel-2 satellite using the spectral response function [34].  
The date 28 March represents the early tillering stage for winter wheat, 28 April is the end of tillering and the start of grain filling, and 2 May is the early grain filling stage for winter wheat. The total ground base measurements were performed at two complete phenological stages of tillering and heading, but also early grain filling data, covering the main growth phenophases of winter wheat. The dates and weather conditions of ground data collection are shown in Table 2.  
Figure 2: Scheme of ground measurement method of _f_PAR.
# PAPR Reduction in UFMC for 5G Cellular Systems
## 4 Numerical Results and Discussions
### BER and EVM Performance  
In this section, we investigate the BER and EVM performances at the presence of both AWGN and Rayleigh fading channels. We apply the proposed Dcomp-SLM to the UFMC system and compare the performance with that of original UFMC and OFDM signals at various IBO values, i.e., IBO = 3, 5, and 7 dB, and for \(U\) equals 6 phase vectors, as depicted in Figures 5-8.  
Figure 4: CCDF-PAPR of UFMC format and OFDM with \(N=512\) and 16-QAM modulation, at different number of phase vectors, \(U=4\), 6, and 8 for both NSLM and Dcomp-SLM schemes.  
Figure 5: Bit error rate (BER) of the proposed Dcomp-SLM comparing to the original UFMC signal, in the presence of additive white Gaussian noise (AWGN) channel, for various input-back-off (IBO) levels at \(N=512\) and \(U=6\) vectors.  
Figure 6: Error vector magnitude (EVM) performances of the proposed Dcomp-SLM comparing to the original OFDM and UFMC signals, in the presence of AWGN channel, for various IBO levels at \(N=512\) and \(U=6\) vectors.  
Figure 7: BER performances of the proposed Dcomp-SLM comparing to the original UFMC signal, in the presence of Rayleigh fading channel, for various IBO levels at \(N=512\) and \(U=6\) vectors.
## 2 Optical Field of DD-MZM Output
### DD-MZM with Basic Configuration
In accordance with the Bessel function, the proportion of carrier and sideband depends on the modulation index \(m\) used. The greater \(m\) is, the carrier power decreases, and more sideband orders are formed. Various forms of modulation schemes can be generated by setting parameters \(\theta\) and \(\gamma\). The OSSB modulation scheme can be generated by setting the value of \(\gamma=\
icefrac{{1}}{{2}}\) and \(\theta=90^{\circ}\). With this arrangement, the lower sideband of order \((4n+1)\) is suppressed, where \(n=0,1,2,\dots\)[15]. The equation \(E_{dual}(t)\) for OSSB modulation is given by  
\[E_{dual-OSSB}(t)=\frac{1}{2}\sqrt{2P_{in}}\Bigg{\{}\sum\limits_{n=-\infty}^{ \infty}j^{n}.J_{n}(m).\left(e^{j\frac{n}{2}}+e^{jn\frac{n}{2}}\right)e^{j2\pi (f_{c}+nf_{m})t}\Bigg{\}} \tag{15}\]
_Appl. Sci._**2013**, \(3\), 39-54; doi:10.3390/app3010039  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
Field Deployable Fiber Bragg Grating Strain Patch for Long-Term Stable Health Monitoring Applications  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
BAM Federal Institute for Materials Research and Testing, Unter den Eichen 87,  
D-12205 Berlin, Germany; E-Mails: [EMAIL_ADDRESS] (N.K.);  
[EMAIL_ADDRESS] (G.K.); [EMAIL_ADDRESS] (W.H.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +49-30-8104-3609; Fax: +49-30-8104-1917.  
_Received: 2 November 2012; in revised form: 7 January 2013 / Accepted: 8 January 2013 / Published: 16 January 2013_  
**Abstract:** A fiber Bragg grating (FBG) strain patch specially adapted for long-term and high-strain applications has been developed and characterized. The design concept for the patch is based on a glass-fiber reinforced plastic (gfrp) carrier material. The developed concept for the FBG integration into the carrier material was derived from reliable integration procedure of FBG sensors into composite structures. The patches' temperature sensitivity, strain gauge factor, fiber-matrix interface adhesion and fatigue behavior were characterized. As a result, FBG strain patches with linear temperature and strain behavior, as well as excellent fatigue resistance, were developed and can be used as part of a monitoring system for advanced composite materials in aerospace structures or wind turbine power plants.  
**Keywords:** fiber Bragg grating; patch; strain gauge factor; temperature sensitivity; push-out test; validation; fatigue behavior
## 5 Data Collection
### Data Set Construction  
#### 5.2.1 Data Structure  
Taking the accelerometer on the mobile phone as an example, it can generate three axial data, namely \(X\), \(Y\) and \(Z\). The data of these three axes will change constantly when the mobile phone is moving, so as to generate the data reflecting the current acceleration state of the mobile phone continuously. The direction of the three axes of the accelerometer relative to the mobile phone is shown in Figure 7.  
The three data generated by the three axes of a sensor on the mobile phone at a certain time point form a data item, and these successively generated data items will form a data sequence, as shown in Figure 8.  
All seven sensors used in the experiment have three axes so that the shape of the generated data are completely consistent. However, the sampling frequency of each sensor is slightly different, so it cannot guarantee that all the sensors have the same sampling time. Consequently, the data generated by different types of physical sensors on the mobile phone cannot be automatically aligned in time, so it is necessary to pre-process the data after the experiment to generate the data set for model training.
# A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model
## 2 Structure of the Combined LSCF-MLE-PMM
### Modal Identification with the LSCF and LSFD Estimators (1 st Step)
#### 2.1.1 The LSCF Estimator  
The first derivation of the LSCF aimed at identifying input-output systems using the measured FRFs as primary data [5; 20]. Afterwards, this derivation was extended to the identification of output-only systems [22]. This technique estimates the invariants of the Common Denominator Model which is assumed to represent the measured FRFs and is given by  
\[\hat{H}_{k}(\Theta,\omega)=\frac{N_{k}(\Theta,\omega)}{d(\Theta,\omega)},\quad k =1,2,\cdots,N_{o}N_{i} \tag{1}\]  
Figure 1: Chart flow of the identification with the combined LSCF-MLE-PMM.  
where \(N_{i}\) and \(N_{o}\) denote the number of inputs and outputs, respectively, with the numerator matrix \(N_{k}(\Theta,\omega)\) and the denominator scalar \(d(\Theta,\omega)\) given, respectively, by  
\[N_{k}(\Theta,\omega)=\sum_{r=0}^{n}\beta_{kr}\Omega_{r}(\omega), \tag{2}\]  
and  
\[d(\Theta,\omega)=\sum_{r=0}^{n}\alpha_{r}\Omega_{r}(\omega) \tag{3}\]  
In Equations (2) and (3) the parameters \(\Omega_{r}(\omega)=z^{r}\) represent discrete-time polynomial base, with \(z=e^{-j\omega\Delta t}\) denoting the \(z\)-domain variable, \(\Delta t\) the sampling time and \(\omega\) the circular frequency. The coefficients \(\alpha_{r}\) and \(\beta_{kr}\) in Equations (2) and (3) are the unknown invariants to be estimated. Detailed discussion about the LSCF technique can be found in several publications and, therefore, it is not discussed in the context of the present paper. Further details about the implementation of this estimator can be found, for instance, in References [5; 20; 22].
## 3 Results and Discussion
Consequently, the amount of light absorbed by the strings owing to the coupling effect increases, resulting in a higher output than that of a typical module. Figure 12 shows the reflectance according to the wavelength of the conventional EVA (UVT)--EVA (UVT)--white backsheet (left) and EVA (UVT)--EVA (UVT)--black backsheet (right) modules. Because of the absorption at the black backsheet, less light is reflected, and the overall performance of the module consequently decreased. Figure 11: Cross-sectional view of the module with EVA (UVT)—EVA (UVT) applied (**left**) and the module with EVA (UVT)—EVA (white) applied (**right**). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **String No.** & **Pm [W]** & **Isc [A]** & **Voc [V]** & **FF [\%]** & **Eff [\%]** \\ \hline \multirow{4}{*}{Before lamination} & 9 & 15.785 & 1.880 & 11.144 & 75.3 & 19.724 \\ \cline{2-7}  & 10 & 15.716 & 1.866 & 11.114 & 75.8 & 19.638 \\ \cline{2-7}  & 11 & 15.777 & 1.863 & 11.134 & 76.1 & 19.714 \\ \cline{2-7}  & 12 & 15.788 & 1.877 & 11.123 & 75.6 & 19.728 \\ \hline \multirow{4}{*}{After lamination} & 9 & 15.565 & 1.913 & 11.036 & 74.9 & 19.449 \\ \cline{2-7}  & 10 & 15.585 & 1.877 & 11.028 & 75.3 & 19.474 \\ \cline{2-7}  & 11 & 15.617 & 1.880 & 11.027 & 75.3 & 19.514 \\ \cline{2-7}  & 12 & 15.638 & 1.889 & 11.019 & 75.1 & 19.540 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Measured string characteristics of a shingled module with white EVA (before and after lamination). As shown in Figure 13, the modules with white EVA exhibited higher reflectivity in the 400-1100 nm range than a typical module, and more reflected light was absorbed by the strings, leading to an increase in the module output. Figure 14 and Table 10 show Pm loss comparisons before and after the lamination process between the conventional EVA (UVT)--EVA (UVT)--white backsheet structure, EVA (UVT)--EVA (white)--white backsheet structure, and EVA (UVT)--EVA (UVT)--black backsheet structure. As the graph shows, the module with white EVA applied had an additional Pm of 4.43% from the rear reflection compared with the black module. Also, compared with the conventional module, a white EVA module resulted in an additional Pm increase of 1.23%. Figure 12: Cross-sectional view of the module with EVA (UVT)—EVA (UVT)—white backsheet (**left**) and EVA (UVT)—EVA (UVT)—black backsheet (**right**) modules. Figure 13: Reflectance according to the wavelength of the EVA (UVT)—EVA (UVT) applied (black graph) and the module with EVA (UVT)—EVA (white) (red graph).
# Multi-Authority Revocable Access Control Method Based on CP-ABE in NDN
## 5 Analysis of Performance
### Calculation Overhead  
In order to analyze the computational cost of the scheme, the proposed scheme is compared with the existing scheme. \(T_{e}\) and \(T_{m}\) are one bilinear calculation time and one exponentiation time. \(l\) is the number of attributes, \(N_{A}\) and \(M\) are the number of authorities and the size of the encrypted file. \(|G_{T}|\) and \(|G|\) are the size of \(G_{T}\) and \(G\). Let \(IA\) denote the universe set of all AA and each AA manages \(xk\) (\(xk(k\in IA)\)) attributes. For a user with uid, let \(xk\), \(uid\) denote the number of attributes obtained from  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Program**} & **Access** & **Can Resist** & **Authority** \\  & **Structure** & **Authorization Center Type** & **Collusion Attacks** & **Revocation** \\ \hline [PERSON] [19] & LSSS & Multiple authorization center & Yes & Indirect revocation \\ \hline [PERSON] [5] & Tree structure & Single authorization center & Yes & User revocation \\ \hline [PERSON] [20] & LSSS & Multiple authorization center & Yes & No \\ \hline [PERSON] [18] & Tree structure & Single authorization center & Yes & No \\ \hline This article & LSSS & Multiple authorization center & Yes & Direct revocation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flexibility comparison of the program.  
the authority with \(\mathit{aid}_{k}(k\in IA)\). The calculation overhead results of each scheme are shown in Table 2. The analysis includes ciphertext size, user encryption time, and user decryption time.  
It can be seen from the results in Table 2 that the ciphertext length of the scheme in this chapter is slightly shorter than the other scheme. the encryption time of the consumer user is also slightly shorter than the other scheme. The user decrypts time because part of calculation is transferred to the NDN cache node in decryption phase. It is significantly shorter than the other. In summary, the computational overhead of this chapter's scheme is significantly better than the other scheme.
# Zero-Voltage Ride-Through Capability of Single-Phase Grid-Connected Photovoltaic Systems
## 3 Overview of PLL Synchronization Techniques  
In the grid-connected PV system, the grid synchronization is very important in the control system, as aforementioned [3; 13; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. A good synchronization can help the system operate at unity power factor in the normal operational mode. Meanwhile, when a grid voltage fault occurs, the PV system should respond immediately by switching to the grid fault operational mode instead of directly shutting down. Therefore, the PLL synchronization techniques should be of fast and accurate dynamics [25; 26; 27; 28; 29]. A general structure of a PLL system is shown in Figure 3. It can be divided into three fundamental blocks: (1) Phase Detector (PD); (2) Loop Filter (LF); and (3) Voltage Controlled Oscillator (VCO). Typically, the LF block is represented by a proportional plus integral (PI) controller to improve the PLL filtering capability. The VCO block is represented as an integrator.  
The basic PD can be a sinusoidal multiplier. The major drawback of the basic PLL with a sinusoidal multiplier is the output containing double-frequency ripples, which are difficult to be eliminated by the LF, even in an ideal case [3]. Hence, many modified single-phase PLL methods are presented in the literature [3; 25; 26; 27; 28; 29], where the tasks have been focused on how to advance the PD unit. Each of the PLL methods has its merits in the normal operational mode. However, in response to zero-voltage phase-to-ground faults, the focuses should be shifted towards evaluating the steady-state phase-angle error, dynamics, harmonic rejection and grid fault detection.  
The orthogonal signal generator (OSG)-based PD and the modified mixer PD using an adaptive notch filtering (ANF) structure are the desirable solutions, mainly beneficial from their simplicity, robustness and effectiveness [25; 26; 27; 28; 29]. Several different structures are established to implement the OSG system or the ANF function. Combined with practical ZVRT operation demands, in addition to the fast dynamics, the capability of grid voltage sag detection is essential. Hence, two representative single-phase PLL methods based on the PD techniques as mentioned above, respectively, will be compared and evaluated.
## 5 Discussions and Future Works
### Future Work
#### 5.2.4 LLM-Web Service Balancing  
While LLMs have the potential to convert existing guidebooks into rule sets, their current capabilities are limited by factors like processing speed and data processing capacity. Hence, they are best suited for normal human conversations and basic analysis but are incapable of handling composite or derived computational tasks yet.  
To overcome these limitations, we showed that LLMs can be integrated with backend web services developed by software developers to handle large datasets and complex computations. This integration will enhance the overall performance and scalability of LLM-powered applications, making them more practical and effective in real-world scenarios. Instead of having the LLM attempt to write its own code, we can instruct it to identify the necessary task and delegate it to a suitable backend service. To achieve this, the backend services should provide the LLM with properly formatted output, enabling the LLM to interpret and utilize the results effectively.  
LLMs, while powerful, cannot solve all problems independently. By using a collaborative approach, where LLMs work in conjunction with specialized services, we can overcome these limitations and create more robust and effective AI solutions. In this work, we tested with only one level of web service functions, but future work can consider a chain of function selection or nested functions. In such cases, the LLM must also decide the sequence of the web service functions to be called and how to transfer the output of one to another as needed.
## 3 Results and Discussion
### Model Performance

Table 2: Overall accuracy and kappa coefficient of the models for classification of celery plants subjected to four leaf nitrogen supplies and three water treatments. LW: low water supply, OW: optimum water supply, HW: high water supply. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **Class** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline \multirow{5}{*}{**MODEL**} & All & 0.806 & 0.065 & 0.861 & 0.806 & 0.797 & \\  & N33 & 0.667 & 0.000 & 1.000 & 0.667 & 0.800 & \\  & N67 & 1.000 & 0.148 & 0.692 & 1.000 & 0.818 & 0.81 \\  & N100 & 0.556 & 0.000 & 1.000 & 0.556 & 0.714 & \\  & N133 & 1.000 & 0.111 & 0.750 & 1.000 & 0.857 & \\ \hline \multirow{5}{*}{**MODEL**} & All & 0.833 & 0.056 & 0.835 & 0.833 & 0.833 & \\  & N33 & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & \\  & N67 & 0.778 & 0.111 & 0.700 & 0.778 & 0.737 & 0.83 \\  & N100 & 0.889 & 0.037 & 0.889 & 0.889 & 0.889 & \\  & N133 & 0.667 & 0.074 & 0.750 & 0.667 & 0.706 & \\ \hline \multirow{5}{*}{**MODEL**} & All & 0.861 & 0.046 & 0.863 & 0.861 & 0.855 & \\  & N33 & 1.000 & 0.074 & 0.818 & 1.000 & 0.900 & \\  & N67 & 1.000 & 0.037 & 0.900 & 1.000 & 0.947 & 0.86 \\  & N100 & 0.667 & 0.037 & 0.857 & 0.667 & 0.750 & \\  & N133 & 0.778 & 0.037 & 0.875 & 0.778 & 0.824 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance results of RF model obtained for classification of sugar beet plants subjected to four nitrogen supply levels and three water treatment levels. LW: low water supply, OW: optimum water supply, HW: high water supply. Numerous papers were dedicated to estimating the plant nitrogen status using multi- or hyperspectral techniques for various research areas, crops, and growth stages [59; 60; 61]. Most of these studies focused on the estimation of nitrogen status based on quantitative approaches, which analyze the relationships with the physiological crop parameters and evaluate their performance in distinguishing the nitrogen levels [50; 62; 63]. This paper is one of only a few studies dedicated to the classification of the plants' nitrogen status [64; 65; 66]. The model accuracies obtained in our work were higher than those obtained by [PERSON] et al. [67].
## 7 Simulation Results
### Predictive Algorithm  
Figure 5 shows the results for energy savings and PER in relation to the study case (complex sequence of movements) for each human subject. It can be seen that, from the energy savings point of view, the reactive algorithm shows the best results for all subjects. However, the difference with the results given by the predictive algorithm is relatively small, being 5% for the most significant case (Subject 2) and only 2% for the others. With respect to PER values, we can see that for both algorithms, it is always lower than 6% for all subjects.  
Table 6 shows the average simulation results for the five subjects previously mentioned. In summary, the reactive algorithm shows an average energy savings better in only 2% with respect to the predictive algorithm, and on the other hand, the predictive algorithm shows an average PER lower in only 1.4% for Link 1 and 0.1% for Link 2, as compared to the reactive algorithm. Therefore, both algorithms provide similar benefits in terms of energy savings and PER, but with respect to the generalization capabilities, the predictive algorithm can be applied to a broader set of subjects with similar performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **TPL n1** & **TPL n2** & **RSSI n1** & **RSSI n2** & **Energy Saving** & **PER n1** & **PER n2** \\ \hline Reactive Optimal & \(-5\) dBm & \(-10\) dBm & \(-71\) dBm & \(-74\) dBm & \(24\%\) & \(5.1\%\) & \(3.3\%\) \\ Predictive & \(-3\) dBm & \(-11\) dBm & \(-71\) dBm & \(-73\) dBm & \(22\%\) & \(3.7\%\) & \(3.2\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation results for the case study. Transmission Power Levels (TPL), node 1 (n1) and node 2 (n2).  
Figure 5: Energy savings and packet error rate for the case study.