esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spatial Patterns in SMAP/CYGNSS \(\beta\) Values  
SMAP/CYGNSS \(\beta\) values (the slopes of the linear regressions of SMAP emissivity and CYGNSS reflectivity) range from 0.002 to \(-\)0.021 dB\({}^{-1}\) (5 th to 95 th percentile) with a median of \(-\)0.007 dB\({}^{-1}\) (Figure 6b). The global spatial patterns in \(\beta\) are similar to the spatial patterns in the median RMSD between 9 km SMAP TBs and 3 km SMAP/CYGNSS TBs (Figure 6c). Regions with low \(\beta\) tend to have high RMSD TBs, and regions with near-zero \(\beta\) tend to have near-zero RMSD TBs. The correlation between \(\beta\) values and median RMSD TBs is \(-\)0.71. Therefore, \(\beta\) (not \(\Delta\Gamma\)) is the primary source of SMAP/CYGNSS TB variations.  
SMAP emissivity and CYGNSS reflectivity are strongly correlated in many areas (Figure 7a). The median global correlation is \(-\)0.58, while the statistical mode of correlations is about \(-\)0.91. Linear regressions with near-zero correlations typically yield near-zero or positive \(\beta\) values (Figure 7a,b). Given the SMAP/CYGNSS TB algorithm (Equation (3)), \(\beta\) values of zero necessitate that \(Tb_{F}\) will equal \(Tb_{\mathbb{C}}\). Therefore, near-zero \(\beta\) values yield low TB spatial heterogeneity. Additionally, as explained in Section 2.2.2, positive \(\beta\) values are unrealistic. Near-zero and positive \(\beta\) values occur in dry areas (e.g., Sahara Desert), forested areas (e.g., Amazon Rainforest), and areas with high topographic roughness (e.g., Andes Mountains). We further investigate the factors that control the spatial patterns in \(\beta\) values below.
# Relating Sentinel-1 Interferometric Coherence to Moving Events on Grasslands
## 2 Materials and Methods
### Mowing Events  
On the grasslands used in this study, the grass was moved to produce fodder. The grass was collected after the moving event. Mowing events were registered using GPS logs from tractors. The start and end times of each mowing event were recorded. The spatial extent of the event was digitised. In total, 77 mowing events were registered during the 2015 vegetation season. There were 27 events in June, 19 in July, 9 in August, and 22 in September. One grassland could be moved several times: 38 first, 30 second, and 9 third mows were registered. The size of the moved area varied between 2.2 ha and 43.2 ha (mean area of 11.9 ha).
## 4 Use of Traffic Simulators to Assess Drivers' Propensity to Exceed Speed Limits
Future research should, therefore, focus on integrating artificial intelligence methods, developing realistic transportation demand models, and gaining a better understanding of the determinants of driver behavior [49]. Simulators are not a one-size-fits-all solution, but when used properly, they can be an effective tool to support the development of safer and more intelligent transportation systems. Traffic simulators are, therefore, very widely used in the analysis and design of transportation systems. They are primarily used for modeling and optimizing vehicle flow, planning road infrastructure, evaluating the effectiveness of traffic signals, testing traffic organization in urban and non-urban conditions, and predicting the impact of transportation investments on the quality of transportation services. They also enable the analysis of emergency scenarios, testing of concepts for autonomous vehicles, and evaluation of the effectiveness of new technical solutions at scales ranging from a single intersection to entire transportation networks. In such applications, the priority is to reproduce traffic dynamics, infrastructure, and operational parameters faithfully. The focus is mainly on the efficiency, safety, and smoothness of traffic in the system dimension. The use of simulators to assess drivers' propensity to exceed speed limits, however, is not among their typical functions but is instead a specialized application used mainly in scientific research or experiments aimed at analyzing the behavior of traffic participants. Standard simulation functions focus primarily on capacity modeling and optimizing the operation of transportation systems. A complete evaluation of speeding tendencies requires a more sophisticated approach, taking into account both psychological and environmental determinants of driver behavior, as well as the calibration of microsimulation models against empirical data. Such analyses are typically based on augmented vehicle tracking models, representing drivers as differentiated agents making individual decisions. They often incorporate elements of artificial intelligence to reflect real-world driving strategies more accurately. Although such functions are not yet widely used in engineering practice, their importance is steadily increasing, particularly in the context of developing intelligent transportation systems, the need for behavioral risk prediction, and the growing role of the human factor in road safety analysis. However, SUMO, thanks to its versatility and configuration capabilities, allows you to influence the parameters of simulations and implemented scenarios. Among the most important elements here is the ability to create custom types of drivers and vehicles, as well as their participation in simulated traffic. When compared with real-world data, this provides a broad foundation for calibrating and adapting the model to actual traffic conditions. This is particularly applicable to the assessment of speed limit violations.
# Improved Altitude Control Algorithm for Quadcopter Unmanned Aerial Vehicles
## 2 Preliminaries
### Quadcopter Control Scheme  
The overall control scheme consists of position and attitude controllers (Figure 2). Conventional PID controllers are used for both horizontal (x- and y-) position control and attitude control while a new algorithm is proposed for altitude control. It is worth noting that our new algorithm can also be used for horizontal position control.
###### Abstract
However, research on the connections between CFT columns and bracing has predominantly focused on column-foundation connection or frame structure. The literature on the application of CFT columns in bridge piers has largely considered single-column designs, emphasizing connections between columns and foundations because the high stiffness of CFT columns can cause concrete foundation failure before reaching the bending capacity. To address this issue, several new connection details have been proposed [16; 17; 18; 19]. For similar reasons, numerous studies have also been conducted on column-beam and column-brace connections [20; 21; 22; 23; 24], primarily focusing on improving the performance of these connections. However, studies specifically addressing configurations applied in bridges remain scarce. In a previous study, a modular bridge substructure featuring composite CFT columns and bracings was proposed [25]. The dimensions and spacing of the CFT columns are carefully calculated to withstand axial force, bending moment, and shear force from design loads. To ensure the integrity of the columns, bracings and their connections must exhibit adequate seismic performance. Lateral bracings restrain the buckling of the CFT columns, whereas the concrete fill restrains the local buckling of the steel tubes. The composite action of CFT modular piers provides high axial stiffness and excellent member ductility, resulting in superior seismic performance [16; 21]. In this study, a modular CFT bridge pier comprising column modules with lateral bracings, pier cap module, and foundation, as shown in Figure 1, was designed and experimentally investigated in three stages. First, small-scale experiments were conducted to investigate the behavior of the connections between the bracing and CFT columns and to identify any issues. Subsequently, component-level tests were performed on the bracing connections to develop a connection design suitable for bridge piers. Finally, this connection design was applied to a full-scale specimen, and its performance was verified.
## 2 Literature Review
### Prediction of Soil Heavy-Metal Content  
In the initial stages of soil pollution prediction research, sample data were often interpolated; the prediction accuracy depended on the number of sampling points. With insufficient sampling data, the prediction accuracy is low. With a complex soil sampling environment and high cost, sampling and analysis of soil heavy-metal content often require significant financial, material, and human resources, and the amount and dimensions of the data are limited. Thus, efficient use of limited soil data is critical in soil pollution prediction research. The rapid development of machine learning has attracted widespread attention; machine learning methods are used to predict soil heavy-metal content.  
[PERSON] used a collaborative composite neural network model using wavelet neural network (WNN) as the basic prediction model to predict the heavy-metal content in soil; at the same time, they proposed a parallel bird swarm algorithm (PBSA) to solve the parameter optimization problem of WNN. The experiment showed that the model could effectively predict the heavy-metal content [13]. [PERSON] proposed a method based on the genetic algorithm and neural network model that integrated soil properties and environmental factors to predict the soil heavy-metal content [14]. With the correlation analysis of heavy-metal content and pretreatment spectral band, the models using Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM), respectively, are constructed to predict the content of heavy metal in soil. The results show that the prediction results of machine learning methods are better than PLS [15]. [PERSON] et al. used a regression kriging method based on natural and anthropogenic factors to predict the Cd content of heavy metals in soil at different locations and analyzed the prediction accuracy of the model [16].  
Although machine learning methods can provide good ideas for predicting soil heavy-metal content, such models have problems including insufficient accuracy and slow convergence speed when predicting pollution. Insufficient consideration of spatial correlation attributes leads to less-than-ideal prediction results. More accurate and efficient prediction models are necessary to address the problems associated with heavy-metal content prediction.
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 1 Introduction
### Brief Layout of the Paper  
The structure of this paper is clear and straightforward. To provide readers with a better understanding of the importance of traffic flow prediction in smart cities and the specific implementation methods of such predictions, this paper first elaborates on the significance of traffic forecasting systems within the context of smart cities. This includes, but is not limited to, mitigating traffic congestion, reducing accident rates, improving the urban environmental quality, and enhancing residents' quality of life. Next, the paper outlines the implementation methods for traffic flow prediction, starting with earlier prediction approaches based on statistical analysis theories and progressing to the latest applications of deep learning methods. A summary of the datasets required for related simulations is also provided. The paper then presents a comparative analysis of methods from different eras through case studies to highlight the most effective approach at present. Finally, the paper concludes by summarizing the advantages and disadvantages of all methods discussed, offering forward-looking perspectives for the future development of traffic flow prediction.
## 3 Hydrological Applications
### Indirect estimation of Hydrological parameters  
The majority of remote sensing contribution in water resources management falls under the indirect hydrological parameter estimation category. The most common area of contribution in this category is the use of classification algorithms to generate land cover classes. Using the unique spectral signature  
Figure 3: The spatial expansion of Devils Lake from Landsat images.  
of land surfaces, mainly in the visible, infrared and thermal spectra, land cover classes are estimated from such data.  
The use of surface temperature, normalized difference vegetation index (NDVI) and unsupervised classification algorithm is demonstrated in mapping land-cover for the Heart River sub-basin, Missouri River basin in North Dakota.
## 5 Conclusions  
In this paper, we propose two FS-based anomaly detection schemes for the detection of CCD assaults in SG communications networks. In the proposed schemes, GA is employed for the selection of discriminative and distinguishing features from historical SE-MF datasets. The selected optimal features are used as the input for two Euclidean distance-based anomaly detection schemes (EDADS-1 for unlabeled data and EDADS-2 for labeled data) to detect anomalies/outliers in the smart-grid SE measurement samples. To validate the performance of the proposed schemes, we utilize the standard IEEE 14-bus, 39-bus, 57-bus and 118-bus systems. In addition, we utilize data that are collected from active power injections into the buses and active power flow measurements in the branches as the learning data and study the accuracy of our detection methods under CCD attack. The test results show that the proposed ED-based FS schemes have reasonably improved detection accuracy, compared to PCA-based FE and NCA-based FS schemes in the occasional operational environment. The low computational complexity of the proposed schemes enables the identification of outliers or anomalies in a short time. In the future, we will model our work by considering more diverse attack scenarios, and we will incorporate a learning mechanism to automatically update the Euclidean distances with incoming test data to improve the detection accuracy.  
All authors conceived of and proposed the research idea. [PERSON] and [PERSON] designed the experiments. [PERSON] performed the experiments. [PERSON]-[PERSON] and [PERSON] analyzed the experimental results. [PERSON] wrote the paper under the supervision of [PERSON]-[PERSON] and [PERSON].  
This work was supported by the 2018 Research Fund of University of Ulsan.  
The authors declare no conflict of interest.
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 2 Background and Related Work  
### Link Failure and Transit-Link DDoS Attack  
A link failure can occur in several ways, such as through natural hazards or a DDoS attack. Rerouting is one solution for such a case. Unlike a traditional DDoS attack, Comelt [17] or CrossFire [18] have shown that, by attacking only the core links, an adversary can effectively degrade the victim's network connectivity or cause it to fail using only a small number of resources. Such DDoS attacks are difficult for a victim to detect because their targets are usually several hops away from the victim's AS and are typically not controllable by the victim. However, if a DDoS attack or a link failure occurs from a link that a victim AS has no control over, rerouting from a traditional Internet environment would be a challenge.
###### Abstract  
Diffusive surfaces are considered as one of the most challenging aspects to deal with in the acoustic design of concert halls. However, the acoustic effects that these surface locations have on the objective acoustic parameters and on sound perception have not yet been fully understood. Therefore, the effects of these surfaces on the acoustic design parameters have been investigated in a real shoebox concert hall with variable acoustics (Espace de Projection, IRCAM, Paris, France). Acoustic measurements have been carried out in six hall configurations by varying the location of the diffusive surfaces over the front, mid, and rear part of the lateral walls, while the other surfaces have been maintained absorptive or reflective. Moreover, two reference conditions, that is, fully absorptive and reflective boundaries of the hall have been tested. Measurements have been carried out at different positions in the hall, using an artificial head and an array of omnidirectional microphones. Conventional ISO 3382 objective acoustic parameters have been evaluated in all conditions. The results showed that the values of these parameters do not vary significantly with the diffusive surface location. Moreover, a subjective investigation performed by using the ABX method with auralizations at two listening positions revealed that listeners are not sensitive to the diffusive surface location variations even when front-rear asymmetric conditions are compared. However, some of them reported perceived differences relying on reverberance, coloration, and spaciousness.  
shoebox concert hall; diffusive surfaces; diffusers location; acoustical parameters; variable acoustics; subjective investigation; auralization +
Footnote †: journal: Applied Physics Letters
## 2 Methodology
### Optimization
#### 2.5.2 The Error Function  
The magnitude error \(E_{m}\) for each configuration is calculated by Equation (13), where \(x_{opt}\) is the vector of the parameters to be optimized, \(S\) is the simulated data, \(M\) is the measured data and \(||\;||^{2}\) is the quadratic norm.  
\[E_{m}(x_{opt})=\frac{||S-M||^{2}}{||M||^{2}}. \tag{13}\]  
The combined magnitude error from the three configurations is computed by Equation (14).  
\[E_{m}^{\text{(comb)}}(x_{opt})=\frac{1}{3}\Big{(}E_{m}^{\text{(AIR)}}+E_{m}^{ \text{(MUT)}}+E_{m}^{\text{(MET)}}\Big{)}. \tag{14}\]  
The angle \(\phi_{d}\) of the phase is calculated by Equation (15), where \(j\) is the imaginary unit, \(f\) is the frequency, \(v\) is the velocity of wave and \(d\) is the distance in meters. One should note that the optimization runs as if only one \(v\) exists in the setup (air)--see the normalization MUT/AIR (\(x_{3}/x_{2}\)) in Equation (12).  
\[\phi_{d}=\text{angle}\Big{(}e^{-j2\pi\frac{f}{\varepsilon}d}\Big{)}. \tag{15}\]  
The adjusted angle \(\phi_{Md}\) of the measured data is given by Equation (16). This shift includes the guided part of the path, which is not modeled in the FEM domain. One should remember that the calibration planes are at the connectors of the radios.  
\[\phi_{Md}=\phi_{M}+\phi_{d}. \tag{16}\]  
The error \(E_{p}\) between the simulated and experimental phase is given similarly to Equations (13) and (14). At last, an overall error \(E_{t}\) can be written in terms of the combined errors and the weighting factor \(w\), as shown in Equation (17).  
\[E_{t}(x)=wE_{m}^{\text{(comb)}}+(1-w)E_{p}^{\text{(comb)}}. \tag{17}\]
# Headspace Solid-Phase Microextraction Analysis of Volatile Components in Peanut Oil
## 2 Results and Discussion
### Optimal Adsorption Time  
For the HS-SPME adsorption fiber and adsorption temperature test, a 50/30 \(\upmu\)m DVB/CAR/PDMS adsorption fiber possessing a better adsorption capacity was used in this study. The test was performed at 10, 20, 30, 40, 50, and 60 min at a water bath temperature of 60 \({}^{\circ}\)C. When examining the total volatile components of Heijings shelled peanuts at adsorption times up to 60 min, the results indicated that the total volatile component content of Heijings shelled peanuts was the highest when the adsorption time was 50 min. The total volatile content of the peanuts did not increase when the adsorption time was increased to 60 min (Figure 3). Therefore, 50 min was chosen for the subsequent experiments.  
Figure 2: Comparison of the contents of the total volatiles in Hekei-140 at different absorption temperatures using HS-SPME. The data correspond to the mean \(\pm\) SD of triplicate experiments. a–c: Values possessing different superscripts are significantly different (\(p<0.05\)).  
When the adsorption extraction time was increased to more than 45 min, the peak area of the volatile components decreased significantly. This is due to observation that the adsorption time affected the extraction efficiency. Extending the adsorption time may affect the sensitivity of the fiber. This phenomenon depends on the affinity of the compound for the fiber. The optimal adsorption time for each compound was due to the competitive absorption of the compound on the fiber coating. Differences have previously been observed [22]; in the HS-SPME analysis process, as the extraction time increases, the analyte diffuses back from the fiber into the headspace and reaches a dynamic equilibrium, ultimately resulting in a decrease in the extraction concentration [23].
## 4 Simulation Results
### Simulation Parameters
To ensure reliable communication, the electrical line length is less than 15 m, and the noise is cyclostationary for NB-PLC. The path number of the RF link is greater than 40 and the noise is modeled with GMM as presented in Equation (8). In Table 3, the authors summarize the characteristic parameters of each communication link, RF and NB-PLC, for the three environments described above. In this paper, the authors choose a topology network with 3 branches between the transmitter A and the receiver H, as shown in Figure 8, where AB, BC, CD, and DH are the lines of the electrical network, whereas E, F, and G are the connected loads whose behaviors are depicted in Figure 10.
# A QoS Classifier Based on Machine Learning for Next-Generation Optical Communication
###### Abstract
[[https://doi.org/10.3390/electronics11162619](https://doi.org/10.3390/electronics11162619)]([https://doi.org/10.3390/electronics11162619](https://doi.org/10.3390/electronics11162619))  
Footnote †: journal: 2022, 111, 2619. [[https://doi.org/10.3390/electronics11162619](https://doi.org/10.3390/electronics11162619)]([https://doi.org/10.3390/electronics11162619](https://doi.org/10.3390/electronics11162619))
benefits in wireless networks and to customers. 6G will also improve technical parameters, such as throughput, support for new high-demand apps, and radio-frequency-spectrum usage using ML techniques [3,4]. The ML techniques are used to enhance the QoS. The rising number of users necessitates a higher bandwidth need for future fixed fifth generation (F5G) connectivity [5]. Optical code division multiple access (OCDMA) can be considered a dominant technique for supporting future applications that require simultaneous transmission of information with high performance [6, 7, 8, 9, 10] among many multiple access techniques, such as time division multiple access (TDMA) and wavelength division multiple access (WDMA). Because time-sharing transmission is essential in TDMA systems, each subcarrier's capacity is limited. Furthermore, transmitting various services, such as audio and data would need time management, which will be difficult [7]. Furthermore, due to the necessity for a high number of wavelengths for users, WDM systems are insufficient for large cardinality systems [6]. The following are the contributions of this paper:  
* The Q-Factor, SNR, and BER are studied as the ML input predictors. The statistical validation of the ML input samples is performed using hypothesis testing. * The multicollinearity of the features is detected using Pearson correlation matrix and the VIF score for each feature. * The ML approach adopted for the classification of the following four classes (SPD: Groups A/D, SPD: Groups B/C, DD: Groups A/D, DD: Groups B/C). The DT and RF are integrating the model as ML classifiers. The evaluation of both classifiers is performed using confusion matrices and ROC curves. The paper is organized as follows. Section 2 is concerned with the model used as the normalization, multicollinearity detection, and ML classifiers are introduced. Section 3 shows the results of each process where the evaluation of the hypothesis testing, Pearson correlation, and VIF are revealed. ML results are evaluated using correlation matrices and ROC curves. In the end, Section 4 presents the conclusion and future work.