esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Polarization Coding Cooperative Communication Scheme for FSO System
## 2 Cooperative Communication Model
When the light radiation at the receiving end is a plane wave, \(\alpha\) and \(\beta\) can be expressed as:  
\[\alpha=\left[\exp\left(\frac{0.49\sigma_{l}^{2}}{\left(1+1.11\sigma_{l}^{12/5} \right)^{7/6}}\right)-1\right]^{-1} \tag{5}\]  
\[\beta=\left[\exp\left(\frac{0.51\sigma_{l}^{2}}{\left(1+0.69\sigma_{l}^{12/5} \right)^{5/6}}\right)-1\right]^{-1} \tag{6}\]  
In Equations (5) and (6), \(\sigma_{R}^{2}\) is Rytov variance, which is defined as:  
\[\sigma_{R}^{2}=1.23C_{n}^{2}k^{7/6}L^{11/6} \tag{7}\]  
\(C_{n}^{2}\) is the atmospheric refractive index structure constant, \(k=2\pi/\lambda\) is the number of light waves, \(\lambda\) is the laser wavelength, and \(L\) is the transmission distance between adjacent nodes.
# Analysis of Azure Zero Trust Architecture Implementation for Mid-Size Organizations
## 7 Comparison of Azure ZTA and Other Well-Known Solutions
### ZTA for Amazon AWS  
AWSs Zero Trust approach revolves around its highly secure Identity and Access Management service. AWS IAM offers precise access control over AWS resources, utilizing policies that govern user capabilities according to specified roles. AWS enhances identity management capabilities by offering services such as AWS Single Sign-On (SSO) and Identity Federation, enabling effortless connection with external identity providers and providing single sign-on access to numerous apps.  
Amazon GuardDuty, AWS CloudTrail, and AWS Security Hub offer ongoing threat detection and monitoring. GuardDuty utilizes advanced algorithms to detect unusual behaviors, while CloudTrail extensively monitors API events. AWS micro-segmentation is achieved using Virtual Private Clouds (VPCs), Network Access Control Lists (NACLs), and AWS Transit Gateway. These components ensure both isolation and secure network segmentation. These services provide real-time monitoring and access control, establishing a robust ZTA for cloud settings.
# A Heuristics-Guided Simplified Discrete Harmony Search Algorithm for Solving 0-1 Knapsack Problem
## 5 Comparative Experiments
### Experiment on the Third Set of Large-Scale 0-1 KP Instances
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Dim** & **Opt** & **Method** & **Best** & **Worst** & **Mean** & **Median** & **Std** & **Time (s)** \\ \hline
200 & 9775 & CGMA & **9775** & **9775** & **9775** & **9775** & 0 & - \\  & & MBPSO & **9775** & **9775** & **9775** & **9775** & 0 & - \\  & & DGHS & **9775** & 9775** & **9775** & **9775** & 0 & - \\  & & DGHS & **9775** & 9775** & 9775** & **9775** & 0 & - \\  & & SDHS & **9775** & **9775** & **9775** & **9775** & 0 & 0.07 \\ \hline \hline \end{tabular} Note: Bold numbers indicate the algorithm found the relatively best solution.  
\end{table} 
Table 15: Comparison of the SDHS algorithm with four metaheuristics on large-scale strongly correlated instances. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Dim** & **Opt** & **Method** & **Best** & **Worst** & **Mean** & **Median** & **Std** & **Time (s)** \\ \hline
200 & 8714 & CGMA & 8714 & 8710 & 8713.00 & 8714 & 1.2318 & - \\  & & MBPSO & 8699 & 8685 & 8692.13 & 8692 & 3.2772 & - \\  & & DGHS & 8672 & 8642 & 8652.80 & 8651 & 6.3702 & - \\  & & HSOSHS & **8714** & 8710 & 8711.47 & 8711 & 1.1366 & - \\  & & SDHS & **8714** & **8714** & **8714** & **8714** & 0 & 0.06 \\
300 & 12,632 & CGMA & 12,632 & 12,626 & 12,629.50 & 12,629 & 1.5702 & - \\  & & MBPSO & 12,588 & 12,570 & 12,577.27 & 12,576 & 4.6382 & - \\  & & DGHS & 12,532 & 12,502 & 12,516.13 & 12,516.5 & 8.4598 & - \\  & & HSOSHS & **12,632** & 12,626 & 12,629.97 & 12,630 & 1.4735 & - \\  & & SDHS & **12,632** & **12,632** & **12,632** & **12,632** & 0 & 0.08 \\
500 & 22,147 & CGMA & 22,141 & 22,128 & 22,135.40 & 22,136 & 3.5292 & - \\  & & MBPSO & 22,053 & 22,010 & 22,025.57 & 22,024.5 & 10.2071 & - \\  & & DGHS & 21,965 & 21,917 & 21,934.33 & 21,930.5 & 12.1324 & - \\  & & HSOSHS & 22,147 & 22,131 & 22,141.47 & 22,142 & 3.7207 & - \\  & & SDHS & **22,148** & **22,148** & **22,148** & **22,148** & 0 & 0.14 \\
800 & 35,749 & CGMA & 35,734 & 35,695 & 35,717.03 & 35,719 & 9.9186 & - \\  & & MBPSO & 35,564 & 35,499 & 35,516.83 & 35,512.5 & 15.7854 & - \\  & & DGHS & 35,469 & 35,368 & 35,394.27 & 35,391 & 20.2006 & - \\  & & HSOSHS & 35,749 & 35,730 & 35,739.23 & 35,740 & 5.7095 & - \\  & & SDHS & **35,762** & **35,762** & **35,762** & **35,762** & 0 & 0.26 \\
1000 & 44,063 & CGMA & 40,402 & 43,996 & 44,018.03 & 44,015.5 & 11.3820 & - \\  & & MBPSO & 33,788 & 43,713 & 43,741 & 43,740 & 15,704.5 & - \\  & & DGHS & 43,626 & 43,571 & 43,598.87 & 43,598.5 & 15.4334 & - \\  & & HSOSHS & 44,063 & 44,024 & 44,047.87 & 44,047.5 & 7.9816 & - \\  & & SDHS & **44,092** & **44,092** & **44,092** & **44,092** & 0 & 0.36 \\ \hline \hline \end{tabular} Note: Bold numbers indicate the algorithm found the relatively best solution.  
\end{table} 
Table 14: Comparison of the SDHS algorithm with four metaheuristics on large-scale weakly correlated instances.
# Experimental Analysis of Ultrasonic Multiple Scattering Attenuation through the Air with Fine Dust
## 4 Experimental Details
### Experimental Procedure  
Using the developed hardware, several ultrasonic multiple scattering experiments were performed. First, the experiment was conducted inside a chamber (400 mm \(\times\) 400 mm \(\times\) 400 mm). As shown in Figure 4, the chamber was made of transparent acrylic plates. Inside the chamber, four fans (5000 rpm) were positioned for circulating the fine dust during the measurement. In addition, a conventionally available environmental meter (K-weather IAQ-Station-LTE) was adopted to measure the relative humidity and temperature during the experiment.  
Figure 2: Ultrasonic multiple scattering hardware: (**a**) schematic illustration of the experimental setup, and (**b**) picture of moving platform using DC-micomotor.  
The measurements were carried out without fine dust, and three cases with different doses (0, 0.004, 0.008, and 0.012 g) were evaluated. The standard particles were carefully weighed using a micro-scale. After pouring the fine dust into the chamber, the fans were switched on and the air quality immediately dropped. The environmental meter confirmed that the applied mass concentration and relative humidity were maintained during the measurement. From the transmitter, five cycles of the torn burst signal (center frequency of 5 MHz) were excited with an amplification of 40 dB. Then, the measured waveform was digitized with a 60 MHz sampling rate and 1500 time points. To eliminate low-frequency noise from the environment, a high-pass filter was applied with a cut-off frequency of 1 MHz. Time averaging was adopted 1000 times at each measurement to remove random electrical noise. The process of data acquisition is presented as follows: (1) the ultrasonic wave was first measured at a 1.7 mm gap between the transmitter and receiver. (2) The receiver was moved away from the transmitter with an interval of 300 \(\upmu\)m. At each interval, data acquisition was repeated 10 times for ensemble averaging. (3) The final length of wave travel was 5 mm, where a total of 120 different time series data were obtained per case of the experiment.
## 1 Introduction  
The use of Wireless Sensor Networks (WSN) has increased substantially in the last years [1]. Both boom of this technology and its versatility have favored the appearance of applications in civil areas (industrial control, environmental monitoring, intensive agriculture, fire protection systems, and so on) and military areas (rescue operations, surveillance, _etc._). On the other hand, the general advances in networks and communications are being constantly applied to WSN, with new ways to implement WSN services and applications appearing. Under the convergence of these technologies (WSN networks, and communications) mobility is an interesting feature that emerges with many possibilities: dynamic sensor networks, mobility of sensors, _etc._ A few cases are exposed next.
# Detection of Volatiles by HS-SPME-GC/MS and Biological Effect Evaluation of Buddha's Hand Fruit
## 2 Materials and Methods
### ABTS Test  
The ABTS-+ radical cation-scavenging activity was determined following [PERSON] et al. [24]. The ABTS-+ radical cation was produced by reacting ABTS 7 mM with potassium persulfate 2.45 mM and keeping the mixture in the dark at room temperature for at least 6 h before use. Then, the ABTS-+ solution was diluted with ethanol to an absorbance of 0.7 (\(\pm\)0.02) at 734 nm and equilibrated at 30 \({}^{\circ}\)C. Then, 1 mL of this solution was mixed for 30 s with 10 uL of each sample (EO, Hy, EEX and MEX). Ethanol and a standard solution of the synthetic antioxidant 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox) were used as negative and positive controls, respectively. Their absorbance was read at 734 nm, at room temperature, 20 s after the end of the mixing using a Jenway 6310 spectrophotometer (Keison, Chelmsford, Essex, UK). Tests were performed in triplicate. The inhibition percentage was calculated, and the results expressed as radical scavenging activity (RSA) determined using the following equation: RSA (radical scavenging activity)% = [(ABS\({}_{\text{control}}-\text{ABS}_{\text{sample}}\))/ABS\({}_{\text{control}}\)] \(\times\) 100.
## Appendix A  
Table A1 (patients) and Table A2 (vocally typical individuals) list the errors of the four Ps estimation methods for each study participant in terms of root-mean-square error with respect to reference Ps values measured using the indirect intraoral equilibration method. For patients, the auditory-perceptual ratings of overall severity are also reported (higher values on the 0-100 scale indicate higher dysphonia).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ID*** & **Method 1** & **Method 2** & **Method 3** & **Method 4** & **Overall Severity** \\ \hline PF1 & 2.45 & 2.03 & 1.65 & 2.61 & 3 \\ PF2 & 2.73 & 1.48 & 1.32 & 1.89 & 14 \\ PF3 & 2.42 & 1.59 & 1.02 & 2.60 & 21 \\ PF4 & 5.81 & 2.11 & 1.56 & 4.12 & 11 \\ PF5 & 1.82 & 1.78 & 1.03 & 1.75 & 50 \\ PF6 & 11.51 & 1.98 & 1.84 & 3.78 & 0 \\ PF7 & 3.35 & 1.63 & 1.42 & 2.81 & 18 \\ PF8 & 2.69 & 2.44 & 2.18 & 2.61 & 37 \\ PF9 & 6.43 & 4.85 & 2.96 & 7.58 & 51 \\ PF10 & 1.74 & 1.11 & 1.09 & 2.00 & 19 \\ \hline NF1 & 4.78 & 1.92 & 1.45 & 4.77 & 23 \\ NF2 & 3.58 & 2.54 & 2.31 & 3.94 & 62 \\ NF3 & 1.37 & 1.06 & 1.05 & 2.29 & 9 \\ NF4 & 7.67 & 3.96 & 3.96 & 8.52 & 93 \\ NF5 & 3.82 & 1.61 & 0.97 & 1.97 & 7 \\ NF6 & 3.05 & 2.61 & 2.23 & 2.51 & 26 \\ NF7 & 3.12 & 0.84 & 0.74 & 1.77 & 4 \\ NM8 & 3.61 & 2.56 & 2.30 & 4.21 & 7 \\ NM9 & 2.90 & 2.03 & 1.19 & 1.71 & 4 \\ NM10 & 3.66 & 1.69 & 1.49 & 3.37 & 18 \\ \hline UF1 & 5.20 & 1.36 & 1.31 & 3.89 & 57 \\ UF2 & 5.87 & 1.23 & 1.10 & 4.34 & 77 \\ UF3 & 2.02 & 1.98 & 1.75 & 2.44 & 56 \\ UF4 & 9.82 & 2.43 & 2.08 & 8.49 & 73 \\ UF5 & 3.17 & 2.57 & 2.42 & 2.34 & 48 \\ UF6 & 2.29 & 1.86 & 0.96 & 1.61 & 95 \\ UM7 & 6.49 & 2.11 & 1.70 & 9.84 & 19 \\ UM8 & 3.24 & 3.15 & 2.88 & 6.02 & 77 \\ UM9 & 6.28 & 1.04 & 0.98 & 5.36 & 10 \\ UM10 & 3.06 & 2.90 & 2.36 & 4.25 & 33 \\ \hline \hline \end{tabular} * First two characters of ID indicate patient type (P = PVH; N = NPVH; U = UVFP) and sex (M = male; F = female).  
\end{table}
Table A1: Error of the four subglottal pressure (Ps) estimation methods for each patient in terms of root-mean-square error (units of cm H\({}_{2}\)O) with respect to reference Ps values measured using the indirect intraoral equilibration method. Reported also are the auditory-perceptual ratings of overall severity.  
Figure 11: Split-violin plots comparing laboratory (left distribution) and ambulatory (right distribution) estimates of Ps for each patient.
## 3 Methodology  
The methodology adopted for the development of the study follows a classical ML flow (Figure 1). The first part was the collection of the data, which were obtained from meters located in hotels in the region of Algarve, Portugal. Then, an EDA was developed, revealing some of the data problems, such as missing and erratic-peaks (Section 4). Some of the revealed problems were solved by the implementation of a data correction module (Section 5). With the initial data analysis transformation applied to the data, some algorithms for anomaly detection were considered. This selection was supported in works such as those ones from [PERSON] and [PERSON] [39], [PERSON] and [PERSON] [40], [PERSON] et al. [41], [PERSON] et al. [42], and [PERSON] et al. [13] (Section 3.1). Then, to evaluate the models, a metric to measure the accuracy and promptness/tardiness of the anomaly detection algorithms was then proposed (Section 3.2). Finally, the tested algorithms were evaluated (Section 6) and an architecture for an anomaly detection intelligent system is proposed (Section 7).
## 4 The Role of UAV-Base Station in Wireless Communications
### Energy Efficiency  
The UAVs can also help enhance the EE of existing cellular networks. This can be achieved by deploying UAVs to assist heterogeneous networks to enhance their capacity faster than their power consumption, which in turn helps to improve their EE. The authors in [105] demonstrated this by deploying millimeter wave UAVs alongside macro and small BSs in a three-tier heterogeneous network to enhance cell edge users throughput, which also led to the overall improvement in the EE of the network. UAVs can also be deployed in existing terrestrial cellular networks to serve delay and rate sensitive users while offloading the traffic of lightly loaded small BSs in order to put them into sleep mode. This approach is known as UAV-assisted BS sleeping strategy and it results in a significant reduction in the energy consumption of the network as was introduced in [106].
## 1 Introduction
Among the existing methods for interpolating XCO2 data, geostatistical methods can fully utilize the spatial and temporal correlation of geographic data and have significant advantages in revealing the spatial trends and patterns of geographic phenomena, while machine learning methods can mine various features of auxiliary data, efficiently process large-scale data, and obtain high-precision estimation results. Combining different models allows for the synergistic utilization of the strengths of each type of model, thereby mitigating the bias inherent in individual models and reducing the volatility of predictive results [29; 30]. However, most current researches rely on a single method to fill gaps in the XCO2 dataset. Therefore, in order to enhance the support of existing discrete XCO2 observations for the studies of spatiotemporal trend of atmospheric CO\({}_{2}\) concentrations, surface carbon emission monitoring, and regional-scale carbon cycling under a space-based perspective, this study considered to construct a novel hybrid model combining deep learning model (CNN) and geostatistical method (spatiotemporal Kriging), termed CNN-STK, to derive fully-covered regional atmospheric CO\({}_{2}\) column concentration data from existing satellite observations. Firstly, the CNN model was constructed by combining a variety of geographic covariates and the OCO-2 XCO2 data in order to extract the deterministic trends in the XCO2 data. Secondly, the residuals of the CNN model were interpolated based on the spatiotemporal Kriging method to optimize the residual distribution [31]. Thirdly, the predicted trends of the CNN model were combined with the residual distributions interpolated by the spatiotemporal Kriging to generate a high-precision monthly XCO2 dataset at 0.25\({}^{\circ}\) grid scale in China from 2015 to 2020. Last but not least, the hybrid CNN-STK model was assessed for accuracy and reliability using multiple validation methods. The objectives and novelties of this study are as follows:  
1. Introducing a novel method to transform discrete satellite observations into continuous spatiotemporal datasets. 2. Effectively integrating different types of models to optimize inherent biases of individual models. 3. Producing an independent, high-precision atmospheric CO\({}_{2}\) dataset to enhance understanding of the carbon cycle and climate change. The paper is organized as follows: Section 2 describes the datasets used in the study and the research methodology. Section 3 shows the experimental results and model evaluations; Section 4 discusses the spatial inhomogeneity, seasonal fluctuations, and uncertainties of CNN-STK XCO2 accuracy, as well as the advantages and limitations of this study.
## 3 Results and Discussion
### Case Study  
#### 3.2.1 Mesh Convergence Test  
The mesh convergence test was conducted to standardize the mesh element size based on the P(2;2) plate configuration with \(h_{s}/t_{s}=1.02\), under \(P_{z}~{}=0.16\) MPa and biaxial compression, which were divided into 100 sub-steps. The results of \(\sigma_{u}\) are shown in Table 8.  
From Table 8, one can infer that the mesh convergence test obtained the same \(\sigma_{u}\) value for element size with 25 mm, 20 mm, and 10 mm. Considering the element size with 25 mm results in a computational effort reduction by 27% and 84% being achieved, respectively. Moreover, the difference in the von Mises stress distribution is depicted in Figure 14.  
Figure 14: The von Mises stress distribution of plates with finite element size of: (**a**) 50 mm, (**b**) 30 mm, (**c**) 25 mm, (**d**) 20 mm, and (**e**) 10 mm.  
Figure 13: Validation of elastoplastic buckling of stiffened plates due combined loading: (**a**) \(U_{z}\) and (**b**) von Mises stress distribution.  
Although in some cases the difference in mesh element size does not impact the \(\sigma_{u}\), there is a difference in the obtained stress distribution. Therefore, based on Table 8 and Figure 14, the mesh element size adopted for all case studies is 25 mm.
## 2 Materials and Methods
### Data Sources
#### 2.2.2 Remote Sensing Data  
The remote sensing data used in this study for 2000-2020 were all derived from MODIS products, namely, EVI, NDVI, GPP, LAI, LST, and land cover (Table 1). The EVI and NDVI were derived from MOD13A2 data, which were available every 16 days with a spatial resolution of 1 km. The gross primary productivity (GPP) and leaf area index (LAI) were obtained from MOD17A2 and MOD15A2H, respectively, both of which were provided every 8 days at a spatial resolution of 500 m. Land surface temperature (LST) data were extracted from MOD11A2, which was also provided every 8 days at a 500 m spatial resolution. The land cover information was sourced from the MCD12Q1 product, which was provided annually with a spatial resolution of 500 m. The data included 17 categories, with our research focusing on forest, grassland, and cropland vegetation types. The remaining categories were considered \"others\" and were not analyzed in this study. The soil moisture data used in this study were obtained from the ERA5-Land reanalysis dataset, covering the period from 2000 to 2020 on a monthly scale with a spatial resolution of 0.1\({}^{\circ}\). We utilized bilinear interpolation to resample all datasets to 0.1\({}^{\circ}\).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Data** & **Time** &
\begin{tabular}{l} **Temporal** \\ **Resolution** \\ \end{tabular} & **Spatial Resolution** \\ \hline Temperature & 2000–2020 & Monthly & 0.1\({}^{\circ}\) \\ Precipitation & 2000–2020 & Monthly & 0.1\({}^{\circ}\) \\ MOD13A2 (NDVI) & 2000–2020 & 16 d & 1000 m \\ MOD13A2 (EVI) & 2000–2020 & 16 d & 1000 m \\ MOD17A2 (GPP) & 2000–2020 & 8 d & 500 m \\ MOD15A2H (LAI) & 2000–2020 & 8 d & 500 m \\ MOD11A2 (LST) & 2000–2020 & 8 d & 1000 m \\ MCD12Q1 (Land cover) & 2000–2020 & Yearly & 500 m \\ ERA5-Land (Soil moisture) & 2000–2020 & Monthly & 0.1\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Climate and remote sensing data.  
Figure 2: Technical flowchart of this study.
Future Internet_**2015**, \(7\), 214-224; doi:10.3390/fi7030214  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**Tracking Positioning Algorithm for Direction of Arrival Based on Direction Lock Loop**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2 and [PERSON] 1**  
1 School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 230022, Anhui, China; E-Mails: [EMAIL_ADDRESS] (D.-R.Z.); [EMAIL_ADDRESS] (P.H.)  
2 CUMT-IoT Perception Mine Research Center, Xuzhou 221008, Jiangsu, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-135-0569-6517; Fax: +86-0551-6382-8101.  
_Received: 23 January 2015 / Accepted: 8 June 2015 / Published: 26 June 2015_  
**Abstract:** In order to solve the problem of poor real-time performance, low accuracy and high computational complexity in the traditional process of locating and tracking of Direction of Arrival (DOA) of moving targets, this paper proposes a DOA algorithm based on the Direction Lock Loop (DILL) which adopts Lock Loop structure to realize the estimation and location of DOA and can adjust the direction automatically along with the changes of a signal's angular variation to track the position of the signal. Meanwhile, to reduce the influence of nonlinearity and noise on its performance, the UKF filter is designed for eliminating interference of the estimated target signal to improve accuracy of the signal tracking and stability of the system. Simulation results prove that the algorithm can not only get a high resolution DOA estimate signal, but can also locate and track multiple mobile targets effectively with enhanced accuracy, efficiency and stability.  
**Keywords:** direction lock loop; DOA Estimate; UKF Filtering; location and tracking
## 4 Distribution of the Subspace-Based Damage Detection Techniques
### Comparison among Identification Methods  
Several subspace system identification methods have been applied for modal identification and VDD of civil structures. These methods are in the form of output-only or input-output algorithms. Output-only algorithms are used for vibration analysis of ambient excited structures. SSI-DATA and SSI-COV techniques are the two main output-only subspace system identification algorithms. SSI-COV algorithm uses the covariance of the raw time-history to reduce the dimensionality of the measurement data. Data reduction in SSI-DATA is performed using QR projection of the Hankel matrix. Both subspace system identification algorithms use SVD to determine the order of a dynamic system. The calculation of the covariance matrix is faster compared to calculation of the QR decomposition which is much slower. However, both algorithms are reported to perform well for the estimation of the modal parameters whereas SSI-DATA is expected to be theoretically more robust due to avoiding squaring up of the measurement data. Combined subspace system identification algorithm is used for identification of system parameters with known input data. More reliable results are obtained by using the input-output subspace system identification compared to the output-only scheme. Several algorithms are introduced based on the classical SSI-COV, SSI-DATA and the combined method to improve the performance of the subspace system identification for SHM application. The performance is enhanced either by change in structure of the underlying algorithms or by adding preprocessing or postprocessing steps to the original subspace system identification algorithm. In some cases, the subspace system identification algorithm is integrated with other analytical methods to yield higher performance.