esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### PLSR Models to Predict Toluene-Adulterated Grignard Reagent  
To accomplish a high predictive performance and overcome probable errors due to data over-fitting, the appropriate selection of latent variables is crucial. As described earlier, four latent variables were selected for the PLSR-based model, which solely depends on the lower error rate. To evaluate the accuracy of the model, the dataset consisting of 90 samples was divided into calibration (50 samples) and validation (40 samples), set in a ratio of 5:4. The coefficient of determination (R\({}^{2}\)) and root mean square error (RMSE) are considered to be the most important factors for evaluating the performance of the model. Thus, the higher the correlation values, the better the calibration method. Figure 3a,b show the original toluene concentrations and the values predicted by the PLSR model for the Grignard reagent with sample concentrations ranging from 0% to 20%. From the figure, it can be seen that the original and predicted values showed an excellent agreement with each other. The calibration model yielded a high R\({}^{2}\) value of 0.94 with a standard error of calibration (SEC) value of 0.65%, whereas the corresponding prediction set yielded R\({}^{2}\) and SEP values of 0.95 and 0.79%, respectively.  
In the multivariate analysis, beta coefficients are useful for the localization of wavenumbers that contain valuable information about chemical features. The value of beta coefficients indicates the extent of change in the predicted value when the corresponding predictor is increased by 1 unit, keeping all  
Figure 3: Partial least squares regression (PLSR) graphs of actual versus predicted concentrations (**a**) calibration plot (**b**) prediction plot.  
other predictors constant. Furthermore, the signs (plus or minus) of the beta coefficients are also used to interpret the direction of the relationship between variables. The higher the beta value, the greater the influence on the predicted value [23]. Figure 4a shows the beta coefficient plot for the developed PLSR model. As a result of comparing the spectra of pure toluene and Grignard reagent shown in Figure 4b, it is observed that the peaks obtained for the toluene come under the same region as the one shown in the beta coefficient plot in Figure 4a. Certain different types of important peaks are observed in the beta coefficients plot (Figure 4a) around 621, 1004, 1210, and 1606 cm\({}^{-1}\) as clearly indicated above in the PCA loading plot [22]. The following regions clearly show the sensitivity of the toluene present in this region.
# An Unambiguous Delay-And-Multiply Acquisition Scheme for GPS L1C Signals
## 3 Results
### New DM Acquisition Schemes for GPS LIC Signal and the Unambiguous Property
#### 3.1.2 The Proposed 33-chip Delay DM Acquisition Scheme
\begin{array}{ll}d_{DM,PP+DD}(\tau)&=\int_{0}^{T_{c}}s_{DM,\delta_{0}}(t+ \tau)\mathcal{S}_{DM,PP+DD}(t)dt\\ &=\int_{0}^{T_{c}}d_{0}[3C_{PP,\delta_{0}}(t+\tau)\pm\sqrt{3}\,C_{DP,\delta_{0} }(t+\tau)SC_{D}(t)SC_{P}(t+\tau-\delta_{0})\\ &\pm\sqrt{3}C_{PP,\delta_{0}}(t+\tau)SC_{P}(t)\,SC_{D}(t+\tau-\delta_{0})+C_{ DD,\delta_{0}}(t+\tau)]\cdot\{C_{PP,\delta_{0}}(t)+C_{DD,\delta_{0}}(t)\}dt\\ &\approx 3d_{0}\int_{0}^{T_{c}}C_{PP,\delta_{0}}(t+\tau)C_{PP,\delta_{0}}(t)dt+ \,d_{0}\int_{0}^{T_{c}}C_{DD,\delta_{0}}(t+\tau)C_{DD,\delta_{0}}(t)dt\\ &=4d_{0}(1-|\tau|)(u(\tau+1)-u(\tau-1))\end{array}  \tag{17}\]  
Since \(C_{PP,\delta_{0}}(t)\) and \(C_{DD,\delta_{0}}(t)\) are PRN signals, the \(d_{DM,PP+DD}(\tau)\) is the ACF of \(C_{PP,\delta_{0}}(t)\) plus the ACF of \(C_{PP,\delta_{0}}(t)\), which has the form of Equation (16), and is shown as Figure 6b. \(\square\)
# Biogenic Nanoparticles: Synthesis, Characterisation and Applications
## 2 Biogenic Synthesis of Nanoparticles
### Types of Nanoparticles
#### 2.1.3 Metal Oxides  
Studies on metal oxides have also been reported including include zinc oxide (ZnO), copper oxide (CuO), magnesium oxide (MgO), titanium dioxide (TiO\({}_{2}\)) and aluminium (Al\({}_{2}\)O\({}_{3}\)), iron oxide (Fe\({}_{3}\)O\({}_{4}\)) and many others. Iron oxide has been a recent and exciting nanomaterial due to its magnetic properties. Iron oxide (Fe\({}_{3}\)O\({}_{4}\)) nanoparticles are being used in different applications including biomedical, catalysis and environmental safety. The most common applications of such NPs include targeted drug delivery, biosensor, MRI, diagnosis of cancer and tissue engineering [35]. Tin oxide (SnO\({}_{2}\)) and tungsten (tri)oxide (WO\({}_{3}\)) have unique electrical properties that depend on the size of these nanoparticles, and these are used as gas sensors [36]. Titanium dioxide finds applications in the optical and solar energy sectors due to its electrical conductivity [37]. Magnesium oxide nanoparticles  
Figure 1: Mechanism of the intracellular-cell bound synthesis of gold nanoparticles (AuNPs) using _L. kimclicus_ DCY51\({}^{\text{T}}\)[28].  
are used to reduce pollution in the air and also catalysts in organic reactions [38; 39]. Copper oxide nanoparticles find applications in the catalysis field including oxidation and photothermal uses [40].
# Novel Real-Time Acoustic Power Estimation for Dynamic Thermoacoustic Control
## 3 Results
### Measurements Conditioning  
The data-acquisition system described in [30] provides pressure measurements that are already expressed in mbar. However, the following steps are needed prior to the calculation of the power delivered to the load.  
First, the acoustic pressure wave and the average pressure value need to be split up. Alternatively, expressing this idea with electrical terms, the alternating current (AC) wave and the DC operation point need to be separated. To do so, the DC component of cycle c is calculated based on the maximum and minimum values of the signal in cycle c \(-\) 1:  
\[X_{DC}(c)=\frac{X_{max}(c-1)+X_{min}(c-1)}{2}, \tag{18}\]  
where \(X_{DC}(c)\) refers to the DC component of signal X in cycle c, whereas \(X_{max}(c-1)\) and \(X_{min}(c-1)\) are the maximum and minimum values of the signal X in cycle c \(-\) 1, respectively (see Figure 6).  
Figure 6: Estimation of the average value of the signal based on the previous cycle.  
This procedure is simple and provide a good estimation of the DC component of the signal, but it cannot be used to calculate the DC component of the first cycle. Therefore, the following expression is used to calculate the DC component for each time step k:  
\[X_{DC}(k)=\frac{\sum_{j=1}^{k}X(j)}{k}, \tag{19}\]  
where \(X_{DC}(k)\) and \(X(k)\) refer to the DC component and the value of signal X in time step k, respectively.  
Figure 7 shows the response of the DC component estimation algorithms in the initial cycle (see Equation (19)) and the rest of the cycles (see Equation (18)).  
Once the AC and DC components have been split up, the AC signal is filtered to reduce the noise using a moving average:  
\[X_{filt}(k)=\frac{\sum_{j=0}^{W-1}X(k-j)}{W}, \tag{20}\]  
where \(X_{filt}(k)\) is the filtered value of the signal and W is the size of the moving window.  
As shown in Figure 8, this filtering technique causes some delay in the filtered signal compared to the original one and, therefore, the size of the moving windows needs to be selected as a trade-off between the noise reduction and the admissible delay.  
Figure 8: Moving-average-based filtering of the drawing, with W = 3.  
Figure 7: Estimation of the average value of the signal based on the first cycle.
## Materials and Methods
### Ground Truth, Monitoring and Environmental Data  
Visual image interpretation was performed in conjunction with extensive ground truth data. The background of the terrain knowledge comes from a survey carried out as part of the DeMarine-1 project in 2008/2009 [10; 30]. In this context, the tidal areas of Norderney were surveyed according to a comprehensive protocol and photographically documented in a 300 \(\times\) 300 m grid of stations. In 2014, sections of the grid were revisited for comparison with the 2008/2009 situation. During the current research on the WIMO project, all of the structures described below have been extensively validated by GPS measurements and photo documentation, in part simultaneously with SAR acquisition (cf. Figure 2). Garmin's GPSmap 62s was used for the GPS measurements, the photos were taken with cameras with GPS functionality. Additionally, the bedforms in the upper flats of Norderney were validated by high-precision height measurements recorded by Real Time Kinematic Differential GPS (RTK-DGPS) with a Leica Differential-GPS SR530 and AT 502 antenna type, see [19; 20]. Furthermore,data from the annual mussel monitoring program of the National Park authority in Lower Saxony (NLPV) were used which are obtained by interpretation of aerial photographs. These data are available as shapefiles and indicate the location and areal extent of the intertidal mussel beds of Lower Saxony (2018). Environmental background information included water level data from the gauges at Borkum Fischerbalje, Norderney Riffgat, Spiekeroog, Wangerooge West and Wangerooge East (source: Federal Waterways and Shipping Administration WSV, provided by Federal Institute for Hydrology BfG), as well as wind speed and direction data from the weather stations on Borkum, Norderney and Spiekeroog (source: German Weather Service DWD).
## 3 Numerical Evaluation  
In this section, a numerical analysis was accomplished for the channel characteristics of hybrid PLC and VLC based on the conventional Lambertian emission beam configuration and the emerging non-Lambertian emission beam configuration. In addition to the abovementioned performance metrics, the overall achievable data rate was also adopted to comparatively evaluate the transmission performance of the investigated hybrid PLC and VLC channels based on distinct optical beam configurations, which is the sum of all achievable data rates from all available subcarriers. The specific expression can be given as follows:  
\[R_{\text{PLC-VLC}}=\sum_{j=1}^{M}\frac{B_{\text{sub}}}{2}\log_{2}\left(1+\frac {\exp(1)}{2\pi}\frac{P_{\text{T}}\big{|}\gamma_{\text{PD}}s_{\text{LED}}H_{ \text{PLC-VLC}}(f_{j})\big{|}^{2}}{P_{\text{N}}}\right), \tag{21}\]  
Figure 3: Schematic of hybrid PLC and VLC based on asymmetric NSPW optical emission beam for the coming 6G IoT network: (**a**) the respective application scenario, (**b**) the respective 3D illustration of beam pattern from a wide side view, (**c**) the respective 3D illustration of beam pattern from a narrow side view. where \(M\) is the number of subcarriers and \(B_{\text{sub}}\) denotes the subcarrier bandwidth. A scaling factor of one-half is included, since a spectral efficiency reduction is induced by the signal processing of Hermitian symmetry, to match the inherent limitations to optical intensity modulation of the VLC portion of this hybrid system. Moreover, \(P_{\text{T}}\) denotes the power allocated to each subcarrier in the electrical domain, \(P_{\text{N}}\) denotes the noise power to each subcarrier, \(\gamma_{\text{PD}}\) denotes the photodiode responsivity at the optical receiver of the VLC portion, and \(s_{\text{LED}}\) denotes the LED conversion factor from the electrical domain to the optical domain. For the convenience of the investigation, the transmitted signal-noise ratio (SNR) is inherited as the figure of merit to evaluate the relevant transmission performance differences between the hybrid PLC and VLC channels of concern, with distinct beam configurations, which is defined in decibels (dB) and specifically given as follows:  
\[\text{SNR}_{\text{T}}=10\log_{10}\left(\frac{P_{\text{T}}}{P_{\text{N}}}\right), \tag{22}\]  
Specifically, one typical medium-sized empty room was considered, which is consistent with Figures 1a, 2a and 3a. In addition, the main parameters involved for the hybrid PLC and VLC wireless system for a 6G IoT network are included in Table 1.  
Here, as for the Lambertian emission beam, the Lambertian index is set as 1 without a loss of generality. The above table summarizes a typical PLC channel with six paths over 30 MHz frequency range, which is consistent with [12]. In the following numerical evaluations, one LED optical transmitter was mounted on the ceiling at the center of the room with the orientation facing towards the floor. The PD-based optical receiver was located along the desktop plane with a straight-upward orientation.
# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
## 4 Initial Attempts  
### Fingerprint Library based Regional Localization  
The fingerprint library is one of the most commonly used methods for indoor positioning. The location fingerprint links the location in the actual environment with a certain fingerprint, and one location corresponds to a unique fingerprint. A fingerprint can be one or more characteristics of a signal. The perceived signal characteristics are then taken to match the signal characteristics in a database, which is essentially a problem regarding supervised learning. The regional localization is to divide the plane or space to be located into small pieces and then locate which region the object is in. We choose RSSI and phase as the characteristics of the signal based on the experience in PRDL [21]. In Figure 2a, the plane is divided into regions by reference tags. Place RFID antennas in four corners and move the tags multiple times to collect the location feature data. The data are prepossessed as training data and are input into the deep neural network to construct a fingerprint database. Figure 2b shows the real bookshelf test scenario [34]. The test results show that, the larger the grid of the region, the higher the localization accuracy. When the grid is \(40\) cm \(\times\) 60 cm, the accuracy is \(95.12\%\).  
Figure 1: Static data fluctuation. (**a**) Received Signal Strength Indication (RSSI); (**b**) Phase; and, (**c**) Doppler Frequency.  
Figure 2: Experimental scenario. (**a**) Regional division scenario; and, (**b**) Bookshelf test scenarioThis method's anti-interference ability is stronger, but it is more difficult to improve the accuracy and is just suitable for scenes with lower localization accuracy requirements. For example, the localization accuracy is only 60.32% when the grid is 20 cm \(\times\) 30 cm. Moreover, the deployment of the system is very difficult, and the portability is weak.
## 3 Conclusions and Future Challenges  
The present review highlighted the use of nanomaterials as electrode surface modifiers in the detection of antibiotics. It discusses the most recent and novel trends in electrochemical sensing and the use of nanoparticles in the detection of antibiotics.  
Furthermore, the sensitivity and practicality of electrochemically sensing antibiotics can be enhanced by further focusing on the fabrication of nanoelectrodes or multi-nanoelectrode arrays. The combination of various advanced nanomaterials to generate a nanoelectrode can lead to an ultra-sensitive electrode surface with far greater potential implementations, notably for in vivo applications.  
As the need for smaller, quicker, cost-effective, and ultrasensitive sample qualification and quantification grows, electrochemical methods and nanoparticles offer a viable path to the next generation of electrochemical sensors. Further advances in the synthesis of nanomaterials and the fabrication of nanoelectrodes have the potential to benefit society not only in the detection of antibiotics but also in a wide range of areas, with the prospect of real-time, point-of-care analyte detection slowly becoming a reality. The requirement for a reproducible, low-cost production process for nanomaterial-based devices on an economically beneficial scale while maintaining the accuracy achieved in a laboratory environment remains a major challenge for the commercial development of such applications.  
Overall, nanoparticle-modified electrodes have demonstrated great potential for the sensitive and selective determination of antibiotic residues in various samples. This field will continue to evolve as new and better methods for analyzing the surface of electrodes are developed. However, further research is needed in order to improve the stability of nanoparticle-modified electrodes. The stability of nanoparticle-modified electrodes can vary depending on the conditions of the electroanalytical measurement. Finally, different strategies need to be developed to overcome interferences from other compounds in order to improve the accuracy of the measurements.  
**Author Contributions:** Conceptualization, S.K. and S.G.; methodology, S.K. and S.G.; software, S.K. and C.S.; validation, S.K., S.G. and C.S.; formal analysis, S.K. and C.S.; investigation, S.K. and C.S.; resources, S.K.; data curation, S.K., C.S. and S.G.; writing--original draft preparation, S.K., C.S. and S.G.; writing--review and editing, S.K., C.S. and visualization, S.K., C.S.; supervision, S.G.; project administration, S.K., C.S. and S.G.; funding acquisition, S.K., C.S. and S.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
The authors declare no conflict of interest.
## 4 Results and Application
### Validation of Coherent Integration Time Optimization Model
#### 4.1.1. Altimetric Sensitivity  
The reciprocal of the altimetric sensitivity simulation is compared with the measured result, as shown in Figure 6. It can be appreciated that the measured and the simulated curves are in good agreement. As the coherent integration time increases, the reciprocal of the sensitivity approaches a limit value. The difference between the curves may be caused by unaccounted noises [33; 34].
## 3 Results
### Adsorption Kinetics  
In this section, the dosage of GA-FK gel was 0.05 g, the initial concentration of Fe(III) solution was 300 mg.L\({}^{-1}\), and the temperature was 303 K. The experimental data were fitted and calculated using the rate equations of pseudo-first-order (Formula (5) [37]) and pseudo-second-order adsorption kinetics (Formula (6) [37]), the results are shown in Figure 9 and Table 3.  
\[\ln(Q_{e}-Q_{t})=\ln Q_{e}-\frac{k_{1}}{2.303}t \tag{5}\]  
\[\frac{t}{Q_{t}}=\frac{1}{k_{2}Q_{e}{}^{2}}+\frac{t}{Q_{e}} \tag{6}\]  
where \(Q_{e}\) (mg.g\({}^{-1}\)) is the experimental value of equilibrium adsorption capacity; \(Q_{t}\) (mg.g\({}^{-1}\)) is the adsorption capacity at t time; \(k_{1}\) (min\({}^{-1}\)) is pseudo-first-order kinetic rate constant; \(k_{2}\) (g\(\cdot\)(mg\(\cdot\)min)\({}^{-1}\)) is the pseudo-second-order rate constant; and \(t\) (min) is the adsorption time.  
It can be seen from Figure 9, the adsorption kinetics of GA-FK gel to Fe(III) fits well with the quasi-second-order kinetic model (R\({}^{2}\) = 1). The calculated equilibrium adsorption quantity value (\(Q_{e2,\alpha l}\) = 142.9 mg.g\({}^{-1}\), calculated by the quasi-second-order kinetic equation) is remarkably close to the experimental value (\(Q_{e,exp}\) = 142.2 mg.g\({}^{-1}\)) (see Table 3). Therefore, it can be concluded that the adsorption process is mainly non-specific  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**T(K)**} & \multirow{2}{*}{**Q\({}_{e,exp}\)**} & \multicolumn{2}{c}{**Pseudo First Order Kinetic Model**} & \multicolumn{2}{c}{**Pseudo Second Order Kinetic Model**} \\ \cline{3-6}  & & \(k_{1}\) & \(Q_{e1,\alpha l}\) & \(R^{2}\) & \(k_{2}\) & \(Q_{e2,\alpha l}\) & \(R^{2}\) \\ \hline
303 & 142.2 & 0.155 & 38.7 & 0.8199 & 0.005 & 142.9 & 1 \\ \hline \end{tabular}
\end{table}
Table 3: Adsorption kinetic fitting parameters of GA-FK gel to Fe(III) in different model.  
Figure 9: Adsorption kinetic fitting curves of GA-FK gel to Fe(III) (pseudo-second-order kinetic model).  
adsorption. The reason is that complexation of phenolic hydroxyl group of GA, free amino and carboxyl of FK with Fe(III), GA has the strongest complexation especially.
## 3 Proposed Converter and PTZ Panorama
### Comparison of JPEG 360 with GPano  
We compared the metadata properties of GPano [35] and JPEG 360 [13]. The following findings are presented:  
JPEG 360 metadata cover all important properties of GPano metadata except capturing and stitching software, photo dates, source image count, and locked exposure. Although these extra properties are related to the stitching process, they are not related to image rendering.  
The JPEG 360 metadata property 'BoxReference' can aid a situation wherein the image or part of the image codestream is supplementary.  
JPEG 360 metadata cover the properties that GPano metadata do not. JPEG 360 PhiGravity, ThetaGravity, and compass phi can provide more information with regard to the rendering device for realistic rendering. JPEG 360 metadata can support multiple viewports. JPEG 360 metadata provide the ViewportRoll property, through which such a viewport whose sides may not be parallel to the sides of the main image can be handled.  
JPEG 360 is built upon the JUMBF super box, which provides compatibility with previous JPEG versions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Property Name** & **Type** & **Default** \\ \hline
**A** & **JPEG360 ImageMetadata** & & \\ \hline \(1\) & JPEG360 Version & Integer & \(1\) \\ \hline \(2\) & MediaType & String & Image/jpeg \\ \hline \(3\) & ProjectionType & String & Equirectangular \\ \hline \(4\) & PhiMin & Real & \(-180\) \\ \hline \(5\) & PhiMax & Real & \(+180\) \\ \hline \(6\) & ThetaMin & Real & \(-90\) \\ \hline \(7\) & ThetaMax & Real & \(+90\) \\ \hline \(8\) & PhiGravity & Real & \(0\) \\ \hline \(9\) & ThetaGravity & Real & \(-90\) \\ \hline \(10\) & CompassPhi & Real & \(0\) \\ \hline \(11\) & BoxReference & String & Conventional \\ \hline \(\mathbf{B}\) & **JPEG360 ViewportMetadata** & & \\ \hline \(1\) & JPEG360 ViewportNumber & Integer & \(0\) \\ \hline \(2\) & ViewportPhi & Real & \(0\) \\ \hline \(3\) & ViewportTheta & Real & \(0\) \\ \hline \(4\) & ViewportPhiFOV & Real & \(100\) \\ \hline \(5\) & ViewportThetaFOV & Real & \(75\) \\ \hline \(6\) & ViewportRoll & Real & \(0\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: JPEG 360 metadata properties.  
Table 6 summarizes the comparison. We can conclude that JPEG 360 metadata include more than GPano metadata for 360-degree images.