esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of Coaxial Jets Mixing Enhancement Using Synthetic Jets
## 2 The Experimental Method
### The Measuring Devices  
In the experiment, the IFA300 hot wire anemometer produced by TSI measured the velocity related data for each position under the control of the traverse measurement system, then the average velocity, streamwise velocity, radial velocity, and turbulence intensity of mainstream were all analyzed. The sampling frequency was 10,000 times per second, and the error of the experimental system with hotwire measurements is 2.3%. The velocity signal was sampled and recorded by the data processing unit.  
The TSI particle image velocimetry (PIV) system with a double-pulse Nd: YAG 120 mJ/pulse was used to monitor and acquire the velocity field. Atomized ethylene glycol was used as a tracer particle. The velocity and vorticity distribution in the range of 80 mm \(\times\) 100 mm in the X-Y plane near the nozzle outlet were continuously collected. Experimental errors were mainly from the probe calibration error, measuring error and mea  
Figure 1: Schematic diagram (**a**) and installation photograph (**b**) of nozzle and actuators, actuator structure diagram (**c**), and actuator outlet centerline velocity distribution (**d**).  
suring position error. The Maximum experimental error with PIV system were controlled within \(\pm\)0.2 m/s.  
Distribution parameters for nozzle A with and without actuators were first measured in the experiment, and the characteristics of the coaxial jets mixing flow field with and without synthetic jets were obtained respectively. Then, a series of comparative experiments were carried out to analyze the mechanism and key factors of coaxial jets mixing enhancement with synthetic jets.
###### Abstract
[7] used the equivalent factor method to calculate the EVs for 25 border counties in Yunnan Province, China, and used the coupling coordination degree and geographic detector to explore the interaction between urbanization level and ESV in the region and to reveal the factors influencing this spatial pattern. Based on land use change, [PERSON] et al. [8] conducted an in-depth analysis of the temporal and spatial evolution characteristics of EVs in the Dongjiang River Basin from 2010 to 2020 and explored the formation mechanism of differences in the spatial distribution of EVs in the Dongjiang River Basin using geographical detector tools. The study areas include the GuangdongHong Kong-Macao Greater Bay Area, the Yellow River Basin, the Yangtze River Delta, and the Minjiang River Basin. [PERSON] et al. [9] revealed the spatial and temporal evolutionary characteristics of the coastal wetland ESV in the Yellow River Delta from 1990 to 2020 and explored the mechanisms impacting the changes in the ESV in this region. [PERSON] et al. [10] reported the spatial and temporal evolution characteristics of the ESV in the Guangdong-Hong Kong-Macao Greater Bay Area from 2000 to 2015 and investigated the mechanisms influencing the ESV in the region. Taking the region along the main stream of the Yellow River as the study area, [PERSON] et al. [11] first conducted a detailed analysis of land use changes in this region and further explored the temporal and spatial evolution characteristics of the regional ESV. [PERSON] et al. [12] explored the evolutionary driving characteristics and direction of ESV by using the Yangtze River Delta urban agglomeration as a research object. [PERSON] et al. [13] conducted a comprehensive assessment of the temporal and spatial changes in the ESV of the Yellow River Basin from 2000 to 2018 and revealed the interaction and coordination between the ESV and the intensity of human activities in this region by combining the coupling coordination degree and geographical detector model. [PERSON] et al. [14] evaluated the ESV of the Minjiang River Basin from 2006 to 2016 and analyzed the change in the law and trade-off synergy of the ESV in the basin. In summary, research on the value of ecosystem services has mostly focused on specific administrative regions or natural river basins, exploring in depth the changes in natural ecology and their impact on service value in these regions [15; 16; 17; 18; 19; 20] but rarely involved the ESV of artificially excavated historical and cultural heritage sites formed by continuous human activities such as those along the Grand Canal. Based on land use data for the Grand Canal region, this study uses the equivalent factor method to evaluate the ESV as well as the spatial and temporal evolution characteristics of cities along the Grand Canal from 1991 to 2021 to provide a reference for the Grand Canal's protection, restoration, and enhancement.
## 3 Results and Discussion
### Myoglobin Denaturation Changes  
The cooked meat color is primarily influenced by heat-induced myoglobin denaturation [50], with a higher degree of denaturation leading to a darker brown appearance [51]. The process of cooking denatures myoglobin, which is responsible for the characteristic dull brown color of cooked meat products. However, the denaturation temperature varies among different redox forms of myoglobin. In meat, myoglobin begins to denature between 55 \({}^{\circ}\)C and 65 \({}^{\circ}\)C, reaching its maximum denaturation at 75-80 \({}^{\circ}\)C [52].  
The HT method significantly impacted the extent of myoglobin denaturation (\(p\leq\) 0.05, Figure 1). The CON-treated sample exhibited the highest level of myoglobin denaturation, approaching nearly 100%, which corresponded with its lowest \(a^{\star}\) values (Table 3). Among all samples, the SV55 treatment resulted in the lowest myoglobin denaturation ratio (33%), followed by the \(\Delta\)T20 sample (38.3%) (Figure 1). The lower level of myoglobin denaturation in the SV and \(\Delta\)T samples explains their higher \(a^{\star}\) values, as shown in Table 3.  
[PERSON] et al. [32] reported that the degree of myoglobin denaturation is influenced by heating temperature, with higher cooking temperatures resulting in a greater denaturation ratio and, consequently, lower \(a^{\star}\) values.  
In the present study, although the \(\Delta\)T40 sample was subjected to the highest HT temperature (110 \({}^{\circ}\)C, calculated as 70 \({}^{\circ}\)C + \(\Delta\)T40), its myoglobin denaturation ratio was lower than that of the CON sample. This could be attributed to the fact that heating was halted as soon as the \(\Delta\)T sample's core temperature reached 70 \({}^{\circ}\)C.
# Decentralized Coordination of DERs for Dynamic Economic Emission Dispatch
## 5 Simulations  
In this section, we show the performance of the proposed decentralized algorithm for the DEED problem based on a test system modified from [40], where we add two ESs into the original ten-unit system. The cost function of DG is quadratic, i.e., \(C_{i}(p_{i})=a_{i}p_{i}^{2}+b_{i}p_{i}+c_{i},\forall i\in\{1,2,\ldots,10\} \rangle,t\in\{1,2,\ldots,24\}\). The cost coefficients and emission coefficients are provided in Table 1. The cost function of the ES is \(C_{i}(p_{i})=d_{i}p_{i}^{2}\), \(\forall i\in\{11,12\})\), \(t\in\{1,2,\ldots,24\}\). The parameters of the ESs are provided in Table 2. The demand during a 24 h horizon is plotted in blue in Figure 2c. For comparison purposes, we first solve the DEED problem with \(w=1\), i.e., a DED problem. Then, we adjust the value of the weight factor from 1 to 0 to study the tradeoff between cost and emission.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Unit** & \(a_{i}\)**(**S/MW\({}^{\text{2h}}\)**) & \(b_{i}\)**(**S/MWb**) & \(c_{i}\)**(**S/h**) & \(a_{i}\)**(**Ib/h**) & \(\beta_{i}\)**(**Ib/MWb**) & \(\gamma_{i}\)**(Ib/h**) & \(\delta_{i}\)**(Ib/h**) & \(\sigma_{i}\)**(I/MW) & \([p_{i}^{\text{min}},p_{i}^{\text{max}}]\) & \(B_{i}\) \\ \hline
1 & 0.1524 & 38.5397 & 786.7988 & 103.3908 & \(-2.444\) & 0.0312 & 0.5035 & 0.0207 & [150, 470] & 0.000049 \\
2 & 0.1058 & 46.1591 & 451.2551 & 103.3908 & \(-2.444\) & 0.0312 & 0.5035 & 0.0207 & [135, 470] & 0.000014 \\
3 & 0.0280 & 40.3956 & 1049.9977 & 300.3910 & \(-4.0695\) & 0.0509 & 0.4968 & 0.0202 & [67, 340] & 0.000015 \\
4 & 0.0354 & 38.3055 & 1243.5311 & 300.3910 & \(-4.0685\) & 0.0509 & 0.4968 & 0.0202 & [60, 300] & 0.000015 \\
5 & 0.0211 & 36.3278 & 1658.5696 & 320.0006 & \(-3.8132\) & 0.0344 & 0.4972 & 0.0200 & [73, 243] & 0.000016 \\
6 & 0.0179 & 38.2704 & 1356.692 & 320.0006 & \(-3.8132\) & 0.0344 & 0.4972 & 0.0200 & [57, 160] & 0.000017 \\
7 & 0.0121 & 36.5104 & 1450.7045 & 330.0056 & \(-3.9023\) & 0.0465 & 0.5163 & 0.0214 & [20, 130] & 0.000017 \\
8 & 0.0121 & 36.5104 & 1450.7045 & 330.0056 & \(-3.9023\) & 0.0465 & 0.5163 & 0.0214 & [47,120] & 0.000018 \\
9 & 0.1090 & 39.5004 & 1455.065 & 350.0056 & \(-3.9024\) & 0.0465 & 0.5475 & 0.0234 & [20, 80] & 0.000019 \\
10 & 0.1295 & 40.5407 & 1469.4026 & 360.0012 & \(-3.9864\) & 0.0470 & 0.5475 & 0.0234 & [10, 55] & 0.000020 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of DGs [40].  
Figure 2: Simulation results of DED. (**a**) Power balance; (**b**) optimal schedule of DGs and ES; (**c**) cut the peak and fill the valley; (**d**) state of charge; (**e**) charging/discharging power; (**f**) evaluation of incremental cost at Hour 18; (**g**) incremental costs within 24 h.
# Machine Failure Prediction Using Survival Analysis
## 5 Results  
The model was tested for its accuracy performance in the machine failure prediction, while also in the prediction of the type of machine failure. This means that the feature ranking/selection layer was also used to measure the best features to calculate each machine failure mode.
# Improving Air Pollution Prediction System through Multimodal Deep Learning Model Optimization
## 3 Dataset
### Satellite Image Dataset  
In this paper, aerosol data [9] was employed for predicting fine dust because it can determine the movement and accumulation according to atmospheric diffusion. Therefore, we constructed a multimodal dataset composed of numerical data and hourly satellite images with aerosol particle size information. The satellite image contains aerosol data for the entire Korean Peninsula so that you can see the overall flow of fine dust on the Korean Peninsula. The aerosol image data after November 2019 are provided at the National Meteorological Satellite Center.  
Figure 1 shows an example of a satellite image with aerosol particle size color legend on the bottom right corner. Aerosol particle size is expressed as \(\alpha\). Particle size is exponential using the ratio of the two wavelengths to the corresponding optical thickness, and the range of \(\alpha\) is \(-0.5\)-\(3\)[10]. In Figure 1, it can be seen that the colors are different depending on the calculation range of \(\alpha\), and the purple series (\(\alpha\): \(-0.5\)-\(0\)) and the blue series (\(\alpha\): \(0\)-\(1\)) represent large aerosols such as yellow dust and sea salt particles. The green series (\(\alpha\): \(1\)-\(2\)) denote medium-sized aerosols, and the yellow and red (\(\alpha\): \(2\)-\(3\)) correspond to fine aerosols, such as pollutants or smoke. That is, the smaller the production range of \(\alpha\), the larger the size of the aerosol particle, and the greater the production range of \(\alpha\), the smaller the size of the aerosol particle.
## 2 Theoretical Background
### Objective Functions
#### 2.1.5 Noise Addition and Mass Normalization  
To investigate the behavior of different objective functions in noisy conditions, random noise has been added as:  
\[\Phi_{j}=\ \Phi_{j}\ (1+\alpha_{noise}\epsilon) \tag{12}\]  
where \(\alpha_{noise}\) is the degree of noise being added, \(\epsilon\) represents a random number between \(-1\) and \(+1\), and \(\acute{j}\) is the component to which noise has been added. The approach has been investigated in eight noise conditions, i.e., 1%, 3%, 5% and 10%in both frequencies and mode shapes and 1%, 3%, 5% and 10% in mode shapes only [16].  
The random noise was added using the MATLAB command _rand_, which uses a standard uniform distribution. Furthermore, for noise addition of 1% to the modal data, the \(\alpha_{noise}\) value has been taken as 0.01 in Equation (12). The noise has been added in the frequencies and mode shapes with respect to the noise-free case. Furthermore, the investigations were done in the frequency domain instead of the time domain in this paper; therefore, noise has been directly added to the frequency domain data.  
Normalization is a process in which scale factors have been applied to mode shapes, such that they can be standardized (scale factors are sometimes applied to natural modes to standardize their elements associated with various DOFs). In certain cases, top floor displacement is taken as unity, whereas in other cases, the largest modal amplitude is taken as unity. However, in this research, mass-normalized mode shapes have been obtained as:  
\[\mathbf{M}_{n}=\mathbf{\phi}^{\prime}\,\mathbf{M}\mathbf{\phi} \tag{13}\]  
such that \(\mathbf{\phi}^{\prime}\,\mathbf{M}\mathbf{\phi}=\mathbf{I}\) where \(\mathbf{I}\) is the identity matrix. The resulting modes are orthogonal, as well as normalized with respect to mass matrix \(\mathbf{M}\).
# Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy
## 3 Results
### SHAP Value Analysis
To further examine the nonlinear relationships identified in Figure 5b, partial dependence plots (Figure 6) were constructed to quantify the individual contribution patterns of the five most influential predictors. Total rainfall exhibited the pronounced threshold behavior previously observed, with SHAP values remaining near zero below 30 mm, escalating sharply within the 30-50 mm range, and reaching maximum contributions exceeding 6 beyond approximately 50 mm before stabilizing at higher precipitation levels. Rainfall intensity followed a comparable nonlinear trajectory, maintaining values between \(-1.5\) and 0 below 21.5 mm/day and then increasing substantially beyond this critical threshold to peak around 1.0 for intensities exceeding 47.9 mm/day. Soil moisture displayed a consistent positive correlation, with SHAP values progressing from \(-0.5\) to 0.5 as moisture content increased from 0.1 to 0.4 m\({}^{3}\)/m\({}^{3}\). Elevation presented an inverse relationship, with values declining from 0.2 to \(-0.4\) as elevation rose from 311.4 to 1549.0 m, remaining predominantly negative above 2000 m. Distance to river revealed proximity-dependent effects, with elevated SHAP values (approximately 0.2) within 1500 m of waterways, transitioning to negative values beyond 4500 m.
## 3 Results
### Model Accuracy and Uncertainty  
The average of three indices (i.e., \(R^{2}\), RMSE, and U) through 50 repetitions showed model performance, i.e., accuracy and uncertainty (Table 3). Compared with the compacted layer, the accuracy in the plough layer was slightly higher both in CV (\(R^{2}\): 0.68 and 0.58 in the plough and compacted layer) and OOB (\(R^{2}\): 0.61 and 0.54). The U (1.09 and 1.05) showed that the uncertainty was also relatively lower in the plough layer. Moreover, the PICP (0.88 and 0.86) confirmed that the 90% \(CI\) derived by QRF was efficacious.  
The spatial distribution pattern of uncertainty was shown in Figures 3a and 4a. There was a great similarity in the spatial distribution of prediction uncertainty at two layers with averages of 1.04 (plough layer) and 1.07 (compacted layer). We found there was lower uncertainty in the middle of Songnen Plain with large areas of croplands. Meanwhile, the relatively higher uncertainty occurred in the western and southwestern areas of Songnen Plain, the north and south sides of Sanjiang Plain, and the west side of Liaohe Plain, where there were relatively scattered fields and varied land-use types (Figures 3 and 4).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Depth**} & \multicolumn{2}{c}{**OOB**} & \multicolumn{2}{c}{**CV**} & \multicolumn{2}{c}{**Uncertainty**} \\ \cline{2-7}  & \(\mathbf{R^{2}}\) & **RMSE (g kg\({}^{-1}\))** & \(\mathbf{R^{2}}\) & **RMSE (g kg\({}^{-1}\))** & **U** & **PICP** \\ \hline PL & 0.61 & 5.45 & 0.68 & 5.10 & 1.05 & 0.88 \\ CL & 0.54 & 4.75 & 0.58 & 4.64 & 1.09 & 0.86 \\ \hline \hline \end{tabular}  
* Plough layer (SOC PL), compacted layer (CL), Quantile Regression Forest (QRF), out-of-bag (OOB), cross-validation (CV), coefficient of determination (\(R^{2}\)), root mean square error (RMSE), prediction interval coverage percentage (PICP).  
\end{table}
Table 3: Performance of QRF on SOC predictions, assessed by \(R^{2}\) and RMSE of CV and OOB datasets, PICP of the CV dataset.  
Figure 2: Raincloud plot of SOC content in the plough layer and the compacted layer.
## 3 Magnetic Data Analysis
### Quantitative Interpretation of Magnetic Anomalies in the Sea of Galilee (Lake Kinneret)  
The Sea of Galilee (Lake Kinneret) is located in the northern Jordan Valley at the central part of the Dead Sea Transform (DST) (Figure 1). This small sea (lake) is Israel's primary source of fresh water. It has an average surface of 165 km\({}^{2}\) and an average volume of 4 \(\times\) 10\({}^{9}\) m\({}^{3}\). Numerous geological investigations show that the rock outcrops in this area and rock samples collected in the wells surrounding the sea range from Jurassic to Quaternary. In contrast to the areas around the sea, the structure below it is not completely understood. Environmental restrictions forbid drilling geological boreholes in the lake.  
Major basaltic volcanism in the Sea of Galilee region has two phases: Early Cretaceous and Late Cenozoic. The Early Cretaceous basalt thickness in the vicinity of the sea is minimal for northern Israel, and consequently, its influence on the magnetic field is negligible [16]. The Late Cenozoic basalts range from the Miocene to the Pleistocene. Most surface basalt outcrops surrounding the sea are of the Pliocene and Pleistocene ages [43].
## 3 Method
### Case Study Approach
\begin{table}
\begin{tabular}{c c} \hline
**Country and Project** & **Project Information** \\ \hline Australia: Sydney Metro Northwest & Sydney Metro Northwest is a part of Sydney Metro, Australia’s largest public transport project. The SMNW project consisted of 36 km rail line and 13 new and upgraded stations. The client Sydney Metro was previously a part of Transport for New South Wales (TfNSW), but has since become an authority of its own, responsible for delivering the full Sydney Metro project. \\  & Three major contracts were awarded in 2013 and 2014 to construct and operate Sydney Metro Northwest. The project opened in May 2019. \\ \hline Australia: Newcastle Light Rail & Newcastle Light Rail is a 2.7 km long railway and a key part of revitalizing the city center of Newcastle. The client was the Transport for New South Wales (TfNSW). The Newcastle Light Rail opened in 2019. \\ \hline Netherlands: A6 Almere & A6 Almere project is part of a program for a motorway between Amsterdam and the city of Almere. This study focused on the fourth of five projects. The client is Rijkswaterstaat (RWS), the Dutch Road, and Waterways Administration. The contractor’s responsibility comprises the design, build, finance, and maintenance (DBFM) of the project. \\  & Construction started in 2017 and the opening is planned for 2022. \\ \hline Sweden: Three road and railway projects & The Swedish case study is based on three road and railway projects: two smaller; and one very large project. The Road 44 project is a new 2 + 1 road in the Southwest of Sweden, opened in 2019. Railway Soderhann-Marmaverken is an upgrading project aimed to increased railway capacity and opened in 2018. Ostlanken (The East Link) is a proposed high-speed railway between Stockholm, Göteborg, and Malmo, and the opening is planned for 2035. In all cases, the Swedish Transport Administration is the client. \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of cases and projects.
###### Abstract  
A radio is adaptive if it can autonomously analyze the communications environment and instantly modify its settings to achieve the best possible efficiency. In orthogonal frequency division multiplexing (OFDM) transmissions, identifying the space frequency block coding (SFBC) category utilized is one of the most important tasks of an adaptive receiver. Previous approaches to this problem did not take into consideration the fact that real systems typically suffer from transmission defects. This study offers a novel maximum likelihood recognizer capable of distinguishing between SFBC OFDM waveforms in the context of inphase and quadrature phase differences (IQDs). The theoretical findings show that IQDs arising from the transmitter and recipient can be combined with channel paths to generate so-called effective channel paths. The conceptual examination demonstrates that the outlined maximum likelihood strategy of the SFBC recognition and effective channel estimation processes is implemented by an expectation maximization tool utilizing the error control decoders' soft outputs. The simulations results reveal that the suggested strategy delivers a much greater recognition accuracy than the typical approaches outlined in the comparable literature. At a signal-to-noise ratio (SNR) of 14 dB, for example, the proposed approach achieves a bit error rate (BER) of 0.00002, which is very close to the case of perfect estimation and compensation for IQDs, outperforming the previous reported works which achieved BERs of 0.01 and 0.02.  
signal recognition; SFBC; maximum likelihood estimation  
13 April 2023  
Revised: 28 May 2023  
Accepted: 30 May 2023  
Published: 1 June 2023
# Numerical Solution for the Single-Impulse Flyby Co-Orbital Spacecraft Problem
###### Abstract  
The traversal inspection of satellites in satellite constellations or geosynchronous orbits has been a focus of research. A large number of variable orbit requirements in the \"single-to-single\" mode severely affects the efficiency of inspections. To address this problem, this study investigated the problem of a single-impulse flyby co-orbiting two spacecraft and proposed a derivative-free numerical solution method that used the geometric relationship between the two intersections of the target and transfer orbits of the flyby problem in order to transform them into a nonlinear equation in a single variable for a given impulse time. The validity of the proposed method was verified using numerical examples. While the Lambert problem is one of the bases for solving the variable orbit problem, on-star intelligent control also raises the requirements for speed. To address this problem, this study also investigated the Lambert problem in a single-impulse flyby co-orbiting two spacecraft and determined the iterative initial value by constructing a quadratic interpolation equation between the inverse of the transfer time and the vertical component of the eccentric vector, the derivative-free quadratic interpolation cut-off method was proposed. Using 100,000 random tests showed that computational efficiency was improved by more than one order of magnitude compared with commonly used methods, with a calculation error of less than \(10^{-6}\).  
numerical solution; flyby multi-target; Lambert problem +
Footnote †: journal: Aerospace