esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Harmful algae blooms (HABs) have been reported with greater frequency in lakes across New York State (NYS) in recent years. In situ sampling is used to assess water quality, but such observations are time intensive and therefore practically limited in their spatial extent. Previous research has used remote sensing imagery to estimate phytoplankton pigments (typically chlorophyll-_a_ or phyocyanin) as HAB indicators. The primary goal of this study was to validate a remote sensing-based method to estimate cyanobacteria concentrations at high temporal (5 days) and spatial (10-20 m) resolution, to allow identification of lakes across NYS at a significant risk of algal blooms, thereby facilitating targeted field investigations. We used Google Earth Engine (GEE) as a cloud computing platform to develop an efficient methodology to process Sentinel-2 image collections at a large spatial and temporal scale. Our research used linear regression to model the correlation between in situ observations of chlorophyll-_a_ (Chl-a) and phyocyanin and indices derived from Sentinel-2 data to evaluate the potential of remote sensing-derived inputs for estimating cyanobacteria concentrations. We tested the performance of empirical models based on seven remote-sensing-derived indices, two in situ measurements, two cloud mitigation approaches, and three temporal sampling windows across NYS lakes for 2019 and 2020. Our best base model (R\({}^{2}\) of 0.63), using concurrent sampling data and the ESA cloud masking--i.e., the QA60 bitmask--approach, related the maximum peak height (MPH) index to phyocyanin concentrations. Expanding the temporal match using a one-day time window increased the available training dataset size and improved the fit of the linear regression model (R\({}^{2}\) of 0.71), highlighting the positive impact of increasing the training dataset on model fit. Applying the Cloud Score+ method for filtering cloud and cloud shadows further improved the fit of the phyocyanin estimation model, with an R\({}^{2}\) of 0.84, but did not result in substantial improvements in the model's application. The fit of the Chl-a models was generally poorer, but these models still had good accuracy in detecting moderate and high Chl-a values. Future work will focus on exploring alternative algorithms that can incorporate diverse data sources and lake characteristics, contributing to a deeper understanding of the relationship between remote sensing data and water quality parameters. This research provides a valuable tool for cyanobacteria parameter estimation with confidence quantification to identify lakes at risk of algal blooms.  
cloud mask; small- and mid-size lakes; maximum peak height (MPH) index
## 1 Introduction
It typically consists of a rubber rod, plastic filler, and anchoring rubber plate. This design addresses the drawbacks of buried water stops, yet its structure is intricate and its cost is elevated [20]. The subsequent advancement in coated surface water stops optimized the anchorage cover plate by incorporating a polyurea coating film [21]. This innovation eliminated the need for an anchorage system, simplifying construction processes. However, the heightened efficiency came at the expense of increased costs [22]. Consequently, there is a need to develop a new type of surface water stop that balances safety, cost-effectiveness, and ease of construction for CSGR dams. The construction of CSGR dams differs significantly from that of RCC dams. This contrast is particularly evident in medium and low dams, where narrow construction sites and faster filling speeds are common requirements. The grouting and vibrating equipment designed for RCC dams [23; 24; 25] cannot be directly applied to CSGR dams because, unlike RCC, CSGR dam materials exhibit variation depending on the available material type for excavation in the reservoir area or nearby locations, and typically lack a fixed gradation. The maximum particle size is 150 mm for dams and up to 300 mm for cofferdam construction; hence, there is high fluctuation in mud and water content during mixing. Consequently, there is a need to develop grouting and vibrating equipment tailored to the characteristics of CSGR materials and the specific requirements of construction site conditions. In this paper, a novel concept is introduced, suggesting the utilization of vibrated grout-enriched CSGR materials as the protection and seepage control layer for CSGR dams. The proposed configuration method is tailored to the vibrated grout-enriched CSGR. Additionally, a new type of T-shaped surface water stop is devised, along with the determination of supporting construction parameters. Furthermore, efficient grouting and vibrating mechanical equipment suitable for narrow warehouse surfaces is developed. The feasibility of the proposed idea is validated through its application in the Tangba Protection Dam of the Qianwei Navigation and Hydropower Hub Project on the Minjiang River and Shaping I Hydropower Station on the Dadu River.
## 3 Materials and Methods
### Material Characterization  
The microstructure and elemental composition of the samples were analyzed using field emission scanning electron microscopy (SEM, JEOL JEM7800F, Peabody, MA, USA) and transmission electron microscopy (TEM, JEOL JEM-2100). The crystalline structure of the samples was examined through powder X-ray diffraction (XRD) using an X-ray diffractometer (Rigaku Miniflex, Rigaku Corporation, Tokyo, Japan) with Cu K\(\alpha\) radiation (\(\lambda\) = 0.15406 nm) over a diffraction angle (20) range of 5\({}^{\circ}\) to 80\({}^{\circ}\). The Raman spectra of the samples were obtained using a Raman spectrometer (LabRAM Aramis, HorBIBA Scientific, Palaiseau, France) to characterize the degree of graphitization and the presence of MoS\({}_{2}\). The N\({}_{2}\) adsorption-desorption isotherms were measured using a Micromeritics ASAP 2020 Plus analyzer (Norcross, GA, USA) for specific surface area (BET) and pore size distribution (BJH) analysis. X-ray photoelectron spectroscopy (XPS) of the samples was performed using a Thermo Escalab 250 spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) with an Al K\(\alpha\) source (hv = 1486.6 eV), and the full spectrum as well as the XPS spectra of individual elements were calibrated using the C 1s peak at 284.6 eV. The ultraviolet-visible (UV-vis) absorption spectra of Li\({}_{2}\)S\({}_{6}\) in the polysulfide adsorption experiments were measured using a PerkinElmer Lambda 6500s spectrophotometer (PerkinElmer, Waltham, MA, USA).
## 4 Experimental Results and Security Analysis
### Security Analysis
#### 4.2.4 Balance Performance Analysis  
The CML map is a nonlinear discrete mapping system, which is similar to the random system. It needs to follow uniform distribution, so the keystream should have good statistical characteristics. The experiments of disequilibrium degree are performed on different lengths and different initial values. The equation of disequilibrium degree \(e\) is defined as the following [10]:  
\[e=\frac{|q_{1}-q_{0}|}{N}\times 100\% \tag{13}\]  
where \(q_{1}\) is in the number of 1 in the sequences and \(q_{0}\) is the number of 0 and \(N\) is the total number of the sequences.  
Table 1 is the simulation results of the original CMLs and the improved CMLs. From the table, we can find that the numbers of 0 and 1 of the improved CMLs are almost balanced and the disequilibrium degrees of the improved CMLs are better than that of the original CMLs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multicolumn{2}{c}{**Length of the**} & \multirow{2}{*}{**Number of 0**} & \multirow{2}{*}{**Number of 1**} & \multicolumn{2}{c}{**Disequilibrium**} \\  & **Sequence** & & & **Degree (\%)** \\ \hline \multirow{3}{*}{(a)} & 10,000 & 4517 & 5483 & 9.66 \\  & 50,000 & 22,509 & 27,491 & 9.64 \\  & 100,000 & 45,085 & 54,915 & 9.83 \\ \hline \multirow{3}{*}{(b)} & 10,000 & 10,000 & 0 & 100 \\  & 50,000 & 50,000 & 0 & 100 \\  & 100,000 & 100,000 & 0 & 100 \\ \hline \multirow{3}{*}{(c)} & 10,000 & 4797 & 5203 & 4.06 \\  & 50,000 & 23,845 & 26,155 & 4.62 \\  & 100,000 & 47,843 & 52,157 & 4.31 \\ \hline \multirow{3}{*}{(d)} & 10,000 & 4754 & 5246 & 4.92 \\  & 50,000 & 23,795 & 26,205 & 4.82 \\ \cline{1-1}  & 100,000 & 47,549 & 52,451 & 4.90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The disequilibrium degree of proposed improved CMLs. (a) The original CML based on logistic map; (b) The original CML based on tent map; (c) The improved CML based on logistic map; (d) The improved CML based on tent map; (e) The improved four stage compound CML, combining both logistic map and tent map in a single stage; (f) The improved four stage compound CML, combining logistic map and tent map in an alternate stage.
# Non Data-Aided SNR Estimation for UAV OFDM Systems
## 3 The Proposed NDA SNR Estimation Method
### SNR Estimation Performance with Different Frequency Offsets  
In order to verify the SNR estimation performance of the proposed method with different frequency offsets. Figure 6 illustrates the effect of different normalized frequency offsets on the estimated SNR under different SNRs. In Figure 6, we observed that the estimation bias increases with the frequency offset due to the increasing introduced interference. As the SNR increases, the influence of the frequency offset on the estimated value increases gradually. The interference introduced by the frequency offset is handled as noise. Under the condition of knowing the signal power, the estimation deviation increases with the SNR since the total noise power becomes enlarge with the frequency offset [26].  
In order to verify that the proposed method is still feasible after effective frequency offset estimation and frequency offset compensation, this paper uses the frequency offset estimation method introduced in Reference [27] to estimate the frequency offset and the frequency offset compensation. Then, the compensated received sequence is used to estimate SNR. Figure 7 describes the comparison of the estimation performance between the circumstances of compensating frequency offset (solid line) and no frequency offset (dashed line). After the frequency offset has been estimated and compensated, the proposed method still performs well (no more than 0.1 dB compared to no frequency offset).  
Figure 4: Comparison of the Input Signal-Noise-Ratio (SNR) with the Estimated SNR.  
Figure 5: Estimation performance with different number of symbols.  
As a result, the proposed SNR estimation method is still effective after the frequency offset has been estimated and compensated.
# IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
## 3 Materials and Methods  
### The Proposed System Architecture  
The hardware part of the system is mainly composed of Raspberry Pi, the sensor network and the Wi-Fi module. The software part is mainly the cloud data storage and client system. Composed of several types of sensors, the sensor network realizes real-time monitoring of the concentration of air pollutants such as SO\({}_{2}\), NO\({}_{2}\), CO, O\({}_{3}\), PM2.5, and PM10. After the periodic sampling is completed, the sensor network sends the various pollutant concentration data to the RPi. As the carrier of edge computing, RPi runs the Kalman Filter algorithm after receiving the data. Then, after several iterations and updates in a very short time, the predicted values of various pollutant concentrations at the next moment are obtained. After completing the relevant prediction work, RPi uploads the data to the cloud through the Wi-Fi module. The cloud stores the data, communicates with the client, and presents the user with information, such as the current air quality status and the trend of each pollutant concentration.
## 3 Coherent \(\pi\)-Electron Angular Momentum and Current of a Molecule Having Two Aromatic Rings
### Two Ring Components of \(\pi\)-Electron Angular Momentum and Current
#### 3.1.2 Coherent Angular Momentum  
The total electron angular momentum operator of an aromatic ring molecule can be expressed as a sum of the angular momentum operator of each aromatic ring, which is defined as \(\hat{O}(\overrightarrow{r})=\overrightarrow{l}_{zL}+\overrightarrow{l}_{zR}\), where \(\overrightarrow{l}_{zK}\) [\(\equiv-ih(x_{K}\partial/\partial y_{K}-y_{K}\partial/\partial x_{K}) \overrightarrow{n}_{K}\)] is the electronic angular momentum operator of the \(z\)-component of ring \(K\) (\(=L\) or \(R\)). Here, \(L\) and \(R\) denote the ring on the left-hand side and that on the right-hand side, respectively. Coordinates \(x_{K}\) and \(y_{K}\) operate on AOs belonging to ring \(K\), and \(\overrightarrow{n}_{K}\) is the unit vector perpendicular to the ring. The expectation value of the angular momentum operator is given, for example, in terms of a 2p\({}_{z}\) carbon AOs as [87]:  
\[\left\langle\overrightarrow{l}(t)\right\rangle=\int d^{3}r_{L}\left\langle \overrightarrow{l}_{zL}\right\rangle+\int d^{3}r_{R}\left\langle \overrightarrow{l}_{zR}\right\rangle\equiv\overrightarrow{l}_{L}(t)+ \overrightarrow{l}_{R}(t), \tag{37}\]  
where  
\[\begin{split}\overrightarrow{l}_{K}(t)&\equiv\int d ^{3}r_{K}\left\langle\overrightarrow{l}_{zK}\right\rangle=-2 nh \overrightarrow{n}_{K}\sum_{\alpha<\beta}\text{Im}\rho_{\beta\alpha}(t) \sum_{ij\in K}\left(\delta_{ab}c_{a^{\prime}i}^{*}c_{b^{\prime}j}+\delta_{a^ {\prime}b^{\prime}}c_{ai}^{*}c_{bj}\right)\\ &\times\frac{x_{K,j}y_{K,j}-x_{K,j}y_{K,j}}{15\sigma_{2}^{2}} \left[3+3\left(\frac{r_{ij}}{a_{2}}\right)+\left(\frac{r_{ij}}{a_{2}}\right)^{ 2}\right]\exp\left(-\frac{r_{ij}}{a_{2}}\right)\end{split}. \tag{38}\]  
Here, \(a_{2}\) is a constant related to the orbital exponent of the 2p\({}_{z}\) AO and \(r_{ij}\) is the distance between \(i\) and \(j\) carbon atom sites.
###### Abstract  
This paper presents a high-precision transfer system of time and RF frequency via the fiber optic link based on secure encryption. On the basis of the two-way time transfer of optical fiber, a security strategy composed of an SM2 encryption algorithm is introduced, which can resist the security risk of time information being tampered with. The experimental results show that the developed picosecond-precision fiber-optic time transfer equipment can ensure high stability while realizing the encryption function. Time synchronization stability in terms of time deviation (TDEV) of 1 PPS can reach around 10.7 ps at 1 s and 7.1 ps at 10 s averaging time. The stability of the 10 MHz frequency can reach around 4.7 \(\times\) 10\({}^{-12}\) at 1 s and 1.1 \(\times\) 10\({}^{-12}\) at 10 s averaging time. There is no significant difference in time transfer accuracy, compared with unencrypted conditions. Furthermore, this paper realizes a ring time transfer network via a 150 km fiber-optic link with three nodes using three devices. The TDEV of 1 PPS can reach around 20.8 ps at 1s averaging time. This paper provides a reference to establish a high-precision, safe, and stable time synchronization fiber network in the future.  
fiber optics; time synchronization; security; high precision +
Footnote †: journal:  
0000-0001-9993-3399]Xinxing Guo  
1 National Time Service Centre, Chinese Academy of Sciences, Xi'an 710600, China; [EMAIL_ADDRESS] (X.G.); [EMAIL_ADDRESS] (B.L.); [EMAIL_ADDRESS] (W.K.); [EMAIL_ADDRESS] (R.D.); [EMAIL_ADDRESS] (S.Z.)
## 3 Results  
### Classification Performance  
The Random Forest model demonstrated strong overall performance for both study areas across all years, with overall accuracy consistently exceeding 95%. Producer's and User's Accuracies were robust, with values surpassing 60% for both regions. The model's effectiveness was further validated by Kappa coefficients and out-of-bag error rates (Tables 4 and 5).  
Despite the generally high performance, certain land cover classes presented challenges. In Porto Velho (Table 5), Floodplain Forests showed the lowest accuracy (78.6% in 2022), while in Manaus (Table 4), Burned areas were the most problematic (64.7% in 2019). These lower accuracies can be attributed to spectral confusion between these specific classes and others. The confusion matrix revealed that in these particular years, Burned Areas were often misclassified as Agriculture/Pastureland, while some Floodplain Forest areas were incorrectly identified as Terra-Firme Forest.  
Analysis of feature importance revealed interesting patterns across the two study areas. In Manaus, the three principal component (PC) features consistently ranked among the most crucial for classification (Figure A1). Conversely, in Porto Velho, 'Elevation' emerged as a key feature, appearing among the most important variables for classification across all years (Figure A2). These findings highlight the distinct characteristics of each region and the adaptability of the Random Forest model in leveraging the most relevant features for accurate classification.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Manaus** & \multicolumn{2}{c}{**2018**} & \multicolumn{2}{c}{**2019**} & \multicolumn{2}{c}{**2020**} & \multicolumn{2}{c}{**2021**} & \multicolumn{2}{c}{**2022**} & \multicolumn{2}{c}{**2023**} \\
**LULC** & **PA\%** & **UA\%** & **PA\%** & **UA\%** & **PA\%** & **UA\%** & **PA\%** & **UA\%** & **PA\%** & **UA\%** & **UA\%** & **UA\%** \\ \hline Terra-Firme Forest & 99.7 & 98.4 & 98.0 & 95.0 & 99.0 & 96.3 & 96.8 & 96.2 & 97.7 & 97.4 & 97.6 & 97.0 \\ Floodplain Forest & 94.2 & 98.3 & 95.4 & 98.4 & 97.6 & 98.4 & 98.4 & 97.7 & 99.3 & 97.1 & 97.6 & 99.1 \\ Secondary Forest & 97.0 & 95.1 & 94.3 & 96.8 & 95.8 & 98.1 & 94.5 & 96.3 & 95.7 & 97.1 & 98.3 & 94.0 \\ Agriculture/Pastureland & 93.8 & 96.6 & 97.7 & 95.0 & 98.0 & 99.2 & 97.2 & 98.4 & 97.6 & 96.0 & 96.2 & 96.9 \\ Burned Areas & 87.5 & 94.6 & 64.7 & 84.6 & 96.8 & 93.8 & 100.0 & 85.0 & 80.8 & 95.5 & 90.6 & 93.5 \\ Barren Land & 100.0 & 94.2 & 98.6 & 98.6 & 98.3 & 98.3 & 96.2 & 96.2 & 97.7 & 95.5 & 95.4 & 96.2 \\ Development Areas & 98.4 & 99.2 & 97.9 & 99.3 & 98.4 & 98.4 & 98.2 & 96.6 & 93.9 & 99.1 & 97.3 & 95.5 \\ Water Bodies & 100.0 & 99.1 & 100.0 & 100.0 & 100.0 & 99.0 & 100.0 & 100.0 & 98.6 & 100.0 & 100.0 \\ \hline Overall Accuracy (\%) & 97.2 & 96.9 & 98.0 & 96.9 & 97.1 & 96.7 \\ Kappa coefficient & 0.97 & 0.96 & 0.98 & 0.96 & 0.97 & 0.96 \\ O-O-B error & 0.03 & 0.03 & 0.03 & 0.03 & 0.03 & 0.03 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy assessment results for the LULC maps of Manaus (PA—Producer’s Accuracy, and UA—User’s Accuracy).
# A Review of Magnetoelectric Composites Based on ZnO Nanostructures
## 3 ZnO Magnetoelectric Composites
### ZnO Micro- and Nano-Rods
ZnO rods were also employed in the fabrication of magnetoelectric sensors based on the piezotronic effect, a remarkable combination of the semiconducting and piezoelectric  
Figure 7: (**a**) SEM images of coated (left and middle) and uncoated (right) ZnO micro needles with Metglas; (**b**) schematic representation of domain wall motion during the magnetization process; (**c,f**) transmission nano XRD experimental setup schematic with an in situ magnetic field; (**d**) SEM image of ZnO hexagonal microrods; (**e**) ZnO (200) peak shift due to intrinsic strain from the FeCoSiB deposition (red) and with additional shift due to the applied magnetic field (blue). (**a,b**) reproduced with permission from [79]. Copyright [2013], AIP Publishing. (**c–f**) adapted with permission from [80]. Copyright [2017], American Chemical Society. properties of ZnO micro- and nanorods [84, 85]. Using the same FTS method as in [80], [PERSON] et al. [36] synthesized mm-long ZnO micro needles (Figure 8b) and fabricated one of the first piezotronic-based magnetoelectric sensors (2-1 connectivity, rod-like PE phase on FM-thin-film) on a FeCoSiB cantilever, shown in Figure 8c. By using the piezotronic principle and piezotronic current measurements with Ohmic contact, an improvement by three orders of magnitude in the limit of detection (LoD) was demonstrated compared to sensors based on the piezoelectric measurements with Schottky contacts (Figure 8d,e). One important outcome is that ZnO resistivity and contact characteristics are essential parameters when considering piezotronic-based devices, as it was shown that the piezotronic current increases while the internal resistance decreases, which is a significant limiting factor of LoD performance. In addition to sensor application, ZnO was also utilized in photocatalytic applications [86], which, when coupled with magnetostrictive materials and visible light irradiation, can induce enhanced photocatalytic degradation of pollutants and increase the disinfection efficiency via the generation of oxygen and hydroxyl radicals [87].
## 2 Data and Methods
### Vertical TEC from the International GNSS Service and Empirical Models
#### 2.4.3 A New TEC Model Based on Principal Component Analysis  
The new Empirical TEC model of [62; 63] based on a 16-year data set of IGS GIMs of vTEC is used as a quiet-time background. This model has been released recently and there is no reported research on its performance, although the authors showed very low residuals with respect to the observational data. In their work, vTEC measurements between 2003 and 2018 were reduced to a lower-dimensional representation through the principal component analysis. The resulting time-expansion coefficients were parameterized in terms of solar and magnetospheric forcing, annual, and LST cycles. The standard deviation of absolute residuals along the EIA crests was reported to be approximately 10 TECU and 3 TECU, and, away from the EIA crests, approximately 2 TECU and 1 TECU, respectively, for the high and the low solar activity periods. The quiet-time condition of the magnetospheric forcing was set in the model with Am = 6 for the experiment. In this way, the diurnal, annual, and solar cycle variations are eliminated, and the residuals mainly show the short-term variations due to magnetospheric forcing, i.e., those variations mainly caused by geomagnetic storms. The Calabia & Jin model is available at Zenodo ([[https://zenodo.org/record/3563463](https://zenodo.org/record/3563463)]([https://zenodo.org/record/3563463](https://zenodo.org/record/3563463)), accessed date: 3 September 2021).
## 5 Evaluation
### Overhead Evaluation  
The reliable multicast loss recovery process includes the feedback and retransmission stages, which cause extra processing overhead to the MTNs. The cache allocation result will affect the overall recovery process of the reliable multicast groups. Therefore, we measure the reasonableness of the cache allocation mechanism in terms of request overhead and recovery overhead. In this paper, the NACK feedback aggregation and recovery isolation mechanism proposed by [20] is used in the implementation of the reliable multicast loss recovery method. We separately count the number of upstream and downstream packets (excluding original multicast data packets) processed by each MTN, and calculate the average of all MTNs. Let the ratio of the above average number of processed packets to the number of original multicast packets sent by the source denote the upstream overhead and the downstream overhead, respectively. This result represents the number of upstream and downstream packets required to reliably deliver a certain number of original multicast packets to the multicast group, respectively.