esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Contribution  
In this paper, a method of soil moisture and organic matter detection based on hyperspectral technology is proposed. This method can be applied to portable hyperspectral spectrometers and unmanned aerial vehicles to achieve rapid detection of soil moisture and organic matter. First, the hyperspectral data of soil moisture and organic matter in the laboratory and in the field were obtained. After wavelet denoising and principal component analysis (PCA) preprocessing, the GA-optimized CNN-LSTM hybrid neural network (GA-CNN-LSTM) algorithm model proposed in this paper was used to predict soil moisture and organic matter. The prediction performance of the two single models, namely CNN and SVR, and the CNN-LSTM hybrid neural network model without GA optimization were compared. The GA-CNN-LSTM algorithm has the highest accuracy of 94.5% and 92.9% for soil moisture and organic matter, respectively. The detection method of soil moisture and organic matter using hyperspectral technology can monitor the distribution of soil moisture and organic matter on a large scale and provide a basis for future rational irrigation and fertilization.  
The rest of this paper is organized as follows. The second part discusses the related work. Section 3 discusses the proposed method in detail. Section 4 discusses the results and discussion. Finally, a conclusion is drawn in Section 5.
## 4 Discussion
\begin{table}
\begin{tabular}{l c c} \hline
**SAMPLE** & **TPC (mg/100 mL) GAE** & **TFC (mg/100 mL) QAE** \\ \hline BT & 113.6 \(\pm\) 2.26 \({}^{\text{a}}\) & 73.04 \(\pm\) 0.66 \({}^{\text{a}}\) \\ OG & 137.16 \(\pm\) 1.06 \({}^{\text{b}}\) & 63.67 \(\pm\) 1.53 \({}^{\text{b}}\) \\ BT+OG (3:1) & 98.18 \(\pm\) 0.34 \({}^{\text{c}}\) & 86.69 \(\pm\) 3.45 \({}^{\text{c}}\) \\ BT+OG (2:1) & 93.74 \(\pm\) 0.88 \({}^{\text{d}}\) & 84.67 \(\pm\) 3.34 \({}^{\text{c}}\) \\ BT+OG (1:1) & 91.82 \(\pm\) 0.67 \({}^{\text{d}}\) & 81.69 \(\pm\) 0.43 \({}^{\text{c}}\) \\ BT+OG (1:2) & 90.33 \(\pm\) 0.55 \({}^{\text{d}}\) & 80.23 \(\pm\) 0.60 \({}^{\text{c}}\) \\ BT+OG (1:3) & 92.34 \(\pm\) 0.37 \({}^{\text{d}}\) & 83.58 \(\pm\) 2.06 \({}^{\text{c}}\) \\ \hline \end{tabular} Data are displayed as the mean \(\pm\) SD (\(n=3\)). BT—black tea, OG—_Ocimum gratissimum_ and BT+OG (black tea + _Ocimum gratissimum_). “Different letters show significant differences at \(p\leq 0.05\) within the same column for a particular type of test. Data are shown as the mean \(\pm\) SD for two independent experiments (each with triplicates for each test point). Different letters show a significant difference (\(p\leq 0.05\))”.  
\end{table} 
Table 2: The total phenolic content (TPC)/total flavonoid content (TFC) of black tea and _O. gratissimum_, alone or in a binary mixture at five different ratios. activities and interactions are influenced by the ratio of bioactive compounds, the nature of the structural properties of the paired compounds, the different radicals, and the reaction mechanisms involved in these assays (LPO, haemolysis, ABTS, and DPPH) [17,18,45].
# A Distributed Real-Time Monitoring Scheme for Air Pressure Stream Data Based on Kafka
## 5 Conclusions  
This paper proposed a real-time air pressure monitoring scheme based on a DSP system. We performed real-time air pressure stream data processing by leveraging the high performance, low delay, scalability, and fault tolerance of Kafka. Large-scale real-time data transmission was reliable, and the dynamic needs of real-time air pressure monitoring were met. Additionally, we also designed an intuitive and user-friendly visualization interface for data analysis and decision making. Simultaneously, the scheme processes the data gathered by sensors according to preset rules and issues alerts for potential anomalies, providing users with real-time, accurate, and reliable services for monitoring air pressure stream data in various environments. In future research, we plan to use machine learning to process and analyze historical data. We will formulate more appropriate data processing rules for different working environments to more accurately and promptly detect anomalies in the environment.  
Conceptualization, Z.C.; methodology, Z.C. and Z.Z.; software, L.Z.; validation, Z.Z.; formal analysis, Z.C. and Z.Z.; investigation, Z.C. and Z.Z.; resources, Z.C.; data curation, Z.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, Z.C. and Z.Z.; visualization, Z.Z. and L.Z.; supervision, Z.Z.; project administration, Z.C.; funding acquisition, Z.C. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
All data are contained within the article.  
The authors thank [PERSON] and [PERSON] for their technical research and development contributions to this project.  
The authors declare no conflicts of interest.
## 4 Experiments
### Evaluation Metrics  
For image quality evaluation metrics, peak signal-to-noise ratio (PSNR) [30] and structural similarity index (SSIM) [31] are the majority of widely utilized indicators for synthetic rainy pictures. PSNR is one of the most widely applied image quality metrics, typically calculated by evaluating the mean squared error (MSE) between the corresponding pixels of two images. For real rainy images, the Natural Image Quality Evaluator (NIQE) [32] and Perception-Based Image Quality Evaluator (PIQE) [33] are employed as criteria for assessing photographic quality that do not need references.
###### Abstract  
This paper presents a novel small-scale system for drinking water treatment from surface waters, designed to rely on gravity as the only source of energy driving the treatment process. The pilot-scale setup, designed for a flow rate of 0.5 L/s, was tested at the Cornell University Water Filtration Plant (CWFP) for a total period of 5 months of operation. The experiments evaluated the influence of selected process parameters on system performance. The identified best operation practices were used to complete a comparative study against CWFP's full-scale treatment process and to conduct a performance assessment in the context of various legislative landscapes. The objective of the work was to determine both the advantages and disadvantages of the proposed technology over established solutions. Over the study period, the average turbidity of the produced water was equal to 0.54 NTU. The pilot compiled with the United States Environmental Protection Agency (US EPA) turbidity standard of <0.3 NTU 47.1% of the time and <1 NTU for 89.9% of the time, thus falling short of the standard of <0.3 NTU 95% of the time and <1 NTU 100% of the time. For 99.5% of the time, it complied with the World Health Organization turbidity guideline of <5 NTU for chlorination treatment. The benchmark conventional system outperformed the tested prototype, complying with the US EPA standards for the entire duration of the study. The tested process also generated a waste stream, which accounted on average for more than 10% of the total raw water volume.  
enhanced coagulation; off-grid water treatment; gravity-powered water treatment plant; surface water treatment; Agua Clara Article
# Carrier-to-Noise-Threshold Filtering on Off-Shore Wind Lidar Measurements
## 3 Data Analysis
### Wind Direction Dependence on Carrier-to-Noise-Ratio  
The wind direction climatology is also affected by the CNR threshold filtering. In Figure 8 wind roses for CNR threshold values of -17 dB, -22 dB, and -35 dB illustrate that the wind direction at FINO3 predominantly is wasesterly to south-westerly. It can also be observed that the wind direction gradually become more westerly and south-westerly for increasing CNR threshold values. It is in agreement with [33] that the wind rose for high wind speed generally shifts towards westerly and south-westerly directions. The effect can be observed in all the cases shown in Figure 8--when only the wind observations from the wind lidar at 126 m are filtered with the CNR threshold (upper panels), when it is applied for the wind-vane measurements at 100 m (second upper panels) and when the whole profile is filtered with the CNR threshold value (Figure 8 second lower panels). Excellent agreement is observed between the wind roses for the wind lidar measurements at 126 m and the wind vane measurements at 100 m. By comparing wind roses for the wind lidar measurements at 626 m  
Figure 7: Standard deviation of the difference between the wind speed at 90 m (\(u_{cup}(90\ m)\)) and 126 m (\(u_{lidar}(126\ m)\)), \(\sigma\big{(}u_{cup}(90\ m)-u_{lidar}(126\ m)\big{)}\), as a function of the CNR threshold. The full line illustrates the relationship without accounting for any wind shear between the measuring heights, the dashed line by use of the logarithmic wind profile to extrapolate the wind speed at 90 m to a height of 126 m.  
(lowest panels) and 126 m veering in the wind direction is observed, which underlines that a wind lidar is an excellent instrument for measuring of directional wind shear.  
Figure 8: Wind roses at the FINO3 off-shore platform for CNR threshold values of –17 dB (right panels), –22 dB (middle panels) and –35 dB (left panels). The upper panels show the wind roses at 126 m when filtering the wind lidar measurements at 126 m height with the actual CNR threshold values, and the next highest panels are for concurrent wind vane observations at 100 m. The second lowest panels illustrate the wind roses at 126 m when the CNR threshold is applied at all levels between 126 m and 626 m, and the lowest panels the wind rose at 626 m when the CNR threshold is applied at 626 m only.  
It can be observed in Figure 9 that the wind roses for a CNR threshold of -29 dB turned out to be almost indistinguishable from the wind roses for -35 dB.
## 3 Results  
### Catalyst Characterization  
#### 3.1.1 Phase Identification and Characterization  
The single phase of ZBO was proved to base on the X-ray diffraction (XRD) (Figure 1). Indeed, the phase of ZBO nano-powders was obtained at 600 \({}^{\circ}\)C almost a single. All peaks are narrow, indicating a good crystallization. The ZBO phase can be determined by diffraction peaks that are in strong agreement with previous research peaks [23; 30]. Moreover, it conforms to the standard card (JCPDS No. 043-0449) of the tetragonal ZnBi\({}_{2}\)O\({}_{4}\)[28]. The crystallite volume was estimated from the highest diffraction peaks using the following Debey-Scherer Equation (2). About 24.16 to 56.18 nm crystalline sizes have been found.  
\[\text{D}=\frac{\text{K}\lambda}{\beta\cos(\theta)} \tag{2}\]  
D is the average crystallite size of the phase under examination in nanometers, K is the Scherrer constant (K = 0.9), \(\lambda\) is the wavelength of the X-ray beam used, \(\beta\) is the full width at half maximum (FWHM) in radians and \(\theta\) is the Braggs angle.  
The ZBO phase could be observed by Fourier transform infrared spectra, as shown in Figure 2. The spinel-shaped metal ions are identified in numerous separate sub-lattices recognized as A-site (tetrahedral) and B-site (octahedral). So, it can be identified in the FTIR analysis by Metal and the closest Oxygen neighbors of each element [41]. In our case,  
Figure 1: XRD diffractogram of ZnBi\({}_{2}\)O\({}_{4}\) calcined at 600 \({}^{\circ}\)C.  
the FTIR spectrum indicates the presence of three strong absorption bands in the region of 400-900 cm\({}^{-1}\), which might be associated with the band of metal-oxygen (M-O) [42; 43; 44]. The presence of these M-O bands means that the tetragonal structure is formed by zinc and bismuth oxides. The presence of peaks between 570 and 628 cm\({}^{-1}\) confirmed the formation of spinel, which belonged to the vibration of the Bi-O bond [45; 46]. The peaks that appear in 430 cm\({}^{-1}\) and 524 cm\({}^{-1}\) could be attributed to the tetrahedral (ZnO\({}_{4}\)) in the catalyst [23; 26].
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 2 UAV Technology in Wireless Communication  
Connected UAVs will be a revolutionary invention that will provide a wide range of services throughout various settings. The requirement for constant connection while on the move is a key issue that must be addressed. Defining the concept of UAVs and HO management is essential. This section provides an overview of UAVs, UAV communication networks, the HO concept, and 3D parameters. The following subsections present an extensive summary of the various subtopics.
## 3 Model Evaluation
### Estimation Results under Low Frequency Probe Data  
For investigation of the deviation between estimation results and ground truth data, estimation results using scatter plot were produced by the four methods on 3% penetration rate of probe data and presented in Figure 3. The figure clearly shows a significant difference during peak periods (6:30-13:00) between estimated travel time by different methods and ground truth travel time. Among all the methods, the estimated travel times from simple mean and moving average and ground truth travel time highly deviate during peak periods. Meanwhile, Bayesian mixture estimation does not result in significant bias. In conclusion, the figure demonstrates that the estimation errors are occurring during peak periods under low sampling rates of probes.  
For better evaluation on the performance of the Bayesian mixture approach, the average absolute and root mean square estimation errors produced during peak periods by the four methods are summarized in Table 2. As shown in the table, the proposed Bayesian mixture method produces the smallest estimation error. Among all the methods, the method of simple mean generally provides the worst performance for all test days, with a significant drop in performance during peak periods. Moving average produces slightly lower estimation errors compared to the simple mean method. Besides, the least standard deviations of MAPE are produced by the proposed Bayesian mixture approach comparedto simple mean and moving average methods. In conclusion, the results show that the proposed algorithm outperforms the different estimation methods during peak periods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & \begin{tabular}{c} **Simple** \\ **Mean** \\ \end{tabular} & **MA(3)** & **MA(5)** & \begin{tabular}{c} **Bayesian** \\ **Mixture** \\ \end{tabular} \\ \hline \multirow{3}{*}{Case I, 29 April} & \begin{tabular}{c} MAPE (\%) \\ RMSE (s) \\ SD of MAPE \\ \end{tabular} & 14.1 & 12.0 & 14.8 & 10.3 \\  & RMSE (s) & 74.9 & 61.5 & 65.6 & 51.2 \\  & SD of MAPE & 13.8 & 10.8 & 11.0 & 9.2 \\ \hline \multirow{3}{*}{Case II, 29 April} & \begin{tabular}{c} MAPE (\%) \\ RMSE (s) \\ SD of MAPE \\ \end{tabular} & 11.8 & 12.8 & 13.7 & 8.6 \\  & RMSE (s) & 31.9 & 29.6 & 32.8 & 21.2 \\  & SD of MAPE & 10.5 & 9.0 & 11.3 & 6.6 \\ \hline \multirow{3}{*}{Case I, 5 May} & \begin{tabular}{c} MAPE (\%) \\ RMSE (s) \\ SD of MAPE \\ \end{tabular} & 19.4 & 19.2 & 21.5 & 13.6 \\  & RMSE (s) & 130.2 & 110.1 & 107.5 & 87.2 \\  & SD of MAPE & 18.4 & 17.8 & 18.1 & 10.9 \\ \hline \multirow{3}{*}{Case II, 5 May} & \
\begin{tabular}{c} MAPE (\%) \\ RMSE (s) \\ SD of MAPE \\ \end{tabular} & 18.8 & 15.5 & 16.5 & 11.2 \\  &
\begin{tabular}{c} RMSE (s) \\ SD of MAPE \\ \end{tabular} & 52.5 & 41.7 & 39.8 & 27.6 \\ \cline{1-1}  & SD of MAPE & 13.9 & 13.2 & 14.3 & 7.5 \\ \hline \end{tabular}
\end{table}
Table 2: Estimation results by different methods during peak periods.  
Figure 2: MAPE by different penetration rates of probe data: (a) Case I, 29 April 2017, (b) Case II, 29 April 2017, (c) Case I, 5 May 2017, (d) Case II, 5 May 2017.
## 6 Conclusions  
The wave passage effect of multi-point wind field is generally considered by the phase of coherence function. However, in the wind field simulation by the spectral representation method, the classical phase formula is not rigorous. This will affect the accuracy of the simulation results and even cause incorrect simulations. In this study, the influences of the phase on four wind field simulations are investigated and discussed in detail. The qualitative analysis based on the theoretical correlation function formula is first made to study the influence of the phase. Then, four numerical examples are utilized to quantitatively study the magnitude of the influence on the sample time history and correlation function of the simulated wind field. Some conclusions are given as follows:  
1. The period of the classical phase is only 50% of the exact result and the amplitude of the classical phase is also only 50% of the exact result.
2. The errors induced by the phase formula occur mainly in the simulation results with high phase participation, such as the samples at the farther simulation points and all cross-correlation functions. The maximum error is even more than 100%. The sample at the first simulation point and all autocorrelation functions are not affected by the phase formula.
3. The classical phase formula has a serious influence on the simulation of four kinds of wind fields. The influence of the phase on the non-ergodic and ergodic stationary wind fields are roughly equivalent. The influence of the phase on the time-varying coherent nonstationary simulation is less than the time-invariant coherent nonstationary simulation.
4. The classical phase formula cannot be used for the wind field simulation, considering the wave passage effect, otherwise it will result in incorrect simulation results and then affect the subsequent structural analysis.  
Conceptualization, N.Z., L.P. and X.W.; methodology, N.Z. and X.W.; software, X.L.; validation, Z.X.; formal analysis, X.C. and X.W.; writing--original draft preparation, N.Z. and X.L.; writing--review and editing, N.Z., X.L. and L.P.; supervision, N.Z. and X.W.; project administration, N.Z. and L.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 52108464 and 51808078), 111 Project of China (Grant No. B18062), the CRSRI Open Research Program (Program SN: CKWV2019733/KY), the Dual Support Plan of Sichuan Agricultural University (Grant No. 2021993510), and the Research Funding for Supporting the Postdoctoral Working in Chongqing (Grant No. 2020 LY06).  
The authors declare no conflict of interest.  
Figure 11: Estimated correlation functions by time-varying coherent nonstationary wind fluctuation samples (1000 samples): (**a**) \(R_{21,21}(t,\tau)\); (**b**) \(R_{1,21}(t,\tau)\).
## 5 Experimental Evaluation
### COVID-19 Vaccination Forecast for the USA
#### 5.3.1 Fully Vaccinated Population Prediction  
The predicted outputs for a period of 60 days (2 months) from our model classifier are shown in Figure 14.  
We also performed a prediction for a shorter period of 7 days. For brevity, the predicted output is shown in Figure 15. The prediction accuracy and error of different models are given in Table 7. (We omit the values for RMSE while discussing our result because RMSE represents unit error instead of percentage error. We did not further process and convert RMSE to percentage error because the other metrics show relative representation as RMSE.) For the near-time forecast, SVM, M5 model tree, and MLP all have 100% DAC. Although RF has the lowest MAPE, RAE, and RMSE, the DAC is below 100%. The population of fully vaccinated people being an aggregation metric will always increase with time and so we prioritize the models having the highest DAC over minimum error metrics. Thus, we chose MLP model with 100% DAC and the lowest error among SVM, M5 model tree and MLP models.  
Figure 14: COVID-19 vaccination prediction to forecast fully vaccinated population in the USA for different machine learning models with forecast time of 2 months.  
For the 2-month prediction period, all models except KNN and LR have 100% DAC. RF again has least errors, but Figure 14d shows that the predicted output is almost flat over the predicted time period. Considering the aggregation metric, we expect the output to increase over time, and thus selected a different model. Of all other methods, SVM has the lower error and is considered as the best classifier for the 2-month forecasting. Thus, using SVM, we can predict that by 25 July 2021 around 48% of the US population will be fully vaccinated (2 doses as applicable according to the vaccine) against COVID-19.
## 2 Methods
### Experimental Setup: Compatibility Testing  
To assess compatibility, a range of configurations representative of real-world conditions were evaluated. The operating systems tested included both older and newer versions of Linux, specifically versions 18.04 and 24.04, due to the predominant use of Linux in server environments. Additionally, deployments were carried out on Windows 10.  
Figure 2: In the upper part, **Development JAVIS** can be seen. Its front-end in Vue.js and back-end with Django communicate through an API endpoint to locally hosted LLMs inside the Docker container. Below, **Production JAVIS** shows Docker Compose powered by the Nginx container, which acts as the central routing hub, directing traffic between the front-end and back-end containers. The front-end container communicates with the back-end container via Nginx to handle dynamic content and API requests. The back-end container, which manages server-side logic, interacts directly with the PostgreSQL container for database operations and with the Celery container for executing asynchronous tasks. Celery may also access PostgreSQL if needed for specific background tasks.  
In terms of graphics hardware, both data center and consumer-grade GPUs were examined, including the NVIDIA A6000 with 50 GB of memory, NVIDIA GeForce RTX 3050 with 8 GB, and NVIDIA GeForce RTX 3060 with 12 GB. Various driver versions were also tested, spanning from the older 535 series to the latest 561 series. Moreover, different CUDA versions, namely 12.2 and 12.6, were utilized in the evaluations.  
For each configuration, the system was deployed, and the installation time was recorded to ensure compatibility and evaluate the speed of setup.
## 3 QualiChain Architecture, Pilot Concept, Research Questions, and Execution Methodology  
### QualiChain Architecture  
The QualiChain project combined blockchain and ontologies with the computational intelligence provided by data analytics and decision support to develop a value-adding platform for education and labor market stakeholders. In the QualiChain architecture, blockchain and ontologies constitute the baseline services and facilitate the storing and verification of education credentials, as well as the creation of smart badges. Data analytics and decision support constitute the value-adding services and are combined with the baseline services to provide intelligent profiling, CV customization, course recommendations, and career advisory applications. One of the key objectives of the project was to showcase the potential of the aforementioned technologies in higher education, pilot the platform in different settings, and generate lessons learned and policy recommendations towards further application of blockchain in the domain. In the following sub-sections, we present the high-level technical architecture of QualiChain and expand on some key concepts, which are the selection of blockchain and the issuance of smart badges in QualiChain.
# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 3 Advancements in Knock Detection
### Pressure Signals
#### 3.1.2 Processing of Pressure Signals
Their study concluded that the algorithm had a fast response time, making it applicable for real-world engine control. Another study by [PERSON] et al. (2018) used deep learning algorithms to develop a model to predict knock at steady-state conditions. Their study found that at steady-state conditions, they were able to achieve a 99 percent prediction accuracy; however, it was unclear if this technique would work at the transient conditions that are often associated with knock.