esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
These findings validate the research methodology and also provide a promising framework for future environmental and hydrological data reconstruction efforts. A Dell Precision 3660 workstation--equipped with an i9-12900K, 16-core 3200 MHz processor and 32 GB of memory--was used for the modelling tasks. Substantial differences in computation speed emerged across the various imputation models. The ratio model proved to be the fastest, filling an average of 332 samples in one second with a 5% data gap. The Kalman filter followed in speed, roughly twice as slow by filling an average of 152 samples per second. By contrast, the ML models were markedly slower: XGB managed 8.5 samples on average, and the GB and RF models each filled 0.80-0.85 samples, while CB was the slowest, at just 0.17 samples per second. The ML models were not sensitive to the proportion of missing data, whereas the ratio model and the Kalman filter displayed different sensitivities: the ratio model took increasingly more time as the gap size grew, whereas the Kalman filter required progressively less time. At a 20% gap, for example, the ratio method filled an average of 78 samples, whereas the Kalman Filter filled 431 samples. The reason is that, as the number of gaps grows, the ratio method becomes increasingly time-consuming due to the need for numerous iterative ratio lookups for each missing data point, whereas the Kalman filter, by skipping its update step when data is absent and thus performing fewer computationally intensive updates, ultimately reduces its runtime as the proportion of missing values rises. Figure 11: Correlation between performance index and slope value deviation to 45\({}^{\text{o}}\) in a case of backward interpolation. Figure 10: Deviation from ideal intercept and slope in case of different imputation methods considering all four gap percentage scenario.
# EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
###### Abstract  
In the high-attention-demanding environment of underground coal mines, distraction is a major cause of unsafe behavior and decreased safety performance. Research on the cognitive neural mechanisms and monitoring of distraction in miners is limited. This study used an electroencephalogram (EEG) to examine the correlation between distraction and brain activity in coal miners, aiming to provide an objective method for monitoring distraction in coal miners. Thirty participants completed a simulated hazard recognition task, using the Sustained Attention to Response Task (SART) and noise to induce distraction. Brain activity was recorded and labeled as focused or distracted based on the correctness of the hazard recognition task. EEG features were extracted and selected, and a Random Forest model for distraction identification was constructed based on the selected features. In the focused state, delta power in the temporal region and theta power in the frontal region increased significantly. In the distracted state, alpha power in the temporal and occipital regions and beta power in the occipital and parietal regions increased. The selected EEG features could be used to identify distraction with 84% accuracy. This method can objectively identify distraction in coal miners, highlighting the potential of using EEG for real-time distraction monitoring.  
coal mine; electroencephalography (EEG); distraction; attention state; identification Article
# Near Infrared Illumination Optimization for Vein Detection: Hardware and Software Approaches
## 3 Results and Discussion
### Evaluation of Square LED Arrangement for the Light Illuminator  
In this experiment, the square LED arrangement was used as justified by the findings in Section 1. Square LED arrangement provides more constant illumination that covers the ROI. Images with different square LED arrangements were processed to compare the performance of each arrangement. For the 5 \(\times\) 5 and 6 \(\times\) 6 arrangements, the light illumination intensity was too bright, producing only black and white images. However, the processed images for 6 \(\times\) 6 arrangements as shown in Figure 14 resemble the human vein arrangement based on [PERSON] et al., 2008 and reveal a clearer image of veins than other square LED arrangements.  
Figure 15 shows that the number of vein counts for HM3X3, CM3X3, HM5X5 and CM5X5 are the same. Figure 15 strongly reveals that a 6 \(\times\) 6 square LED arrangement provides the best outcome for the arm area.  
Figure 13: Images for palm (P) and arm (A) area with addition of diffuser.  
The use of multiple LEDs would result in an overlapping illumination pattern. Experiments have shown that various square LED arrangements of the LEDs will alter the  
Figure 14: Images for palm and arm area for square LED arrangement.  
Figure 15: Hessian method for square LED arrangement.  
radiation intensity distribution. This is mainly because the system uses an absorption method, and the camera must be placed on the same side as the light source. In this experiment, the increase in square LED arrangement not only increases the illumination area but also enlarges the shadow from the light source. This study reveals that increasing the square LED arrangement of the NIR is not to be perceived as one of the significant design parameters in the vein visualization process. This statement can be supported by the observation of difficulty in the penetration of light towards the arm area due to the thicker epidermis of the skin. Furthermore, thicker skin can cause the wavelength to degrade its energy as it passes through a deeper layer of the skin.
## 1 Introduction
The core reason is that the existing stability analysis method for VSC is qualitative instead of quantitative. The stability analyses and optimization of VSCs are generally based on stability analysis methods to judge whether VSC is stable [2; 4; 17; 20]. However, these analyses cannot give an intuitive indication of the stability margins of the VSC under different operations. Therefore, the studies on stability analysis cannot clearly and quantitatively describe system instability which makes it difficult to provide intuitive quantitative advice on parameter optimization for VSCs. To overcome the above issue, this article proposes a quantitative stability evaluation method based on analyzing the relationship between the system stability level and the phase angle of the stability evaluation point and clarifies the stable region of the control parameters. Then, the quantitative stability evaluation method is employed to guide the parameter optimization of the active damping controller and PLL. With the optimization, the stable boundary of the VSC parameters can be effectively expanded to avoid oscillations. The proposed method can quantitatively evaluate the stability of VSC system under different parameters. It can be applied to guide the design of the system control parameters and provide quantitative analysis for the precise selection of control parameters for VSCs in practical engineering. This article is organized as follows. Section 2 deduces the impedance model of VSC by considering the asymmetric structure of PLL, then a quantitative stability evaluation method is proposed. Based on the quantitative stability evaluation, Section 3 establishes an active damping method in the current control loop and proposes a PLL parameter optimization method. Section 4 illustrates the simulation results to validate the effectiveness of the proposed method. Finally, the conclusions and contributions of this article are summarized in Section 5.
## 2 Water Resources Monitoring in Semi-Arid Regions: The WARM-SAR Project
### Monitoring of Water Volumes Retained at Reservoirs  
The areas covered by water were extracted by the intensity SAR images despeckled using an optimal weighting multitemporal De Grandi filter [28]. This technique allows amplifying the water--land contrast and producing more accurate information on the extension of water surfaces. A further speckle reduction was obtained by a spatial multilooking, which reduced the images' resolution to 9 m, in accordance with DEM resolution.  
Figure 1: Level-1\(\alpha\) product detail of a small basin near to the Aoérama settlement (test image 31 August 2010).  
The coupled measures of water surface and DEM led to the estimation of water volumes retained by the dam. The retrieved data were used to validate a simple hydrological model [15]. The time evolution of the water storage was computed by the implementation of a balance between the incoming and outgoing water flows.  
In order to validate the model, the computed storages are compared with those extracted from SAR data. In spite of the assumptions made and of the uncertainties linked to the estimation of some of the input data, the model was able to catch the overall behavior of the system and, therefore, can be used for the simulation of different scenarios of water abstraction from the reservoirs and, therefore, to optimize the resource management. Another important application of the proposed model is the estimation of the impact of small reservoirs on the downstream flow, which a crucial information in case of water conflicts [8].
## 4 Numerical Simulation
### Model Parameters  
The model only considers the effects of gravity without any additional loads. The Mohr-Coulomb constitutive model was applied to the surrounding rock and the reinforced zone, while the elastic constitutive model was used for the initial support, temporary support, feet-lock bolts, and bolts. The physical and mechanical parameters of the surrounding rock in the numerical calculation model were selected based on the survey and design data of the newly built Shimentangshan tunnel and the recommended value in the Specifications for Design of Highway Tunnels [33]. And referring to the relevant literature [34], the physical and mechanical parameters of the surrounding rock were enhanced accordingly. Table 4 lists the physical and mechanical parameters of the surrounding rock and the reinforcement area.  
The initial support structure and temporary support structure primarily consist of shotcrete and a steel frame. When conducting numerical simulations, the support structure is treated as a unified entity, with the modulus of elasticity and density of the steel frame converted to match those of the shotcrete. The calculation formula is presented in Equations (1) to (2), and the physical and mechanical parameters of the support structure post-conversion are detailed in Table 5.  
\[E=E_{h}+\frac{A_{g}\times E_{g}}{A} \tag{1}\]  
\[\gamma=\gamma_{h}+\frac{A_{g}}{A}(\gamma_{g}-\gamma_{h}) \tag{2}\]  
where \(E\) is the equivalent elasticity modulus of initial support, \(E_{g}\) is the elasticity modulus of steel frame, \(A_{g}\) is the cross-sectional area of steel frame, \(E_{h}\) is the modulus of elasticity of shotcrete, \(A\) is the total cross-sectional area, \(\gamma\) is the equivalent gravity of initial support, \(\gamma_{g}\) is the gravity of steel frame, and \(\gamma_{h}\) is the gravity of shotcrete.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Gravity/kN\(\cdot\)m\({}^{-3}\)** &
\begin{tabular}{c} **Elasticity** \\ **Modulus/GPa** \\ \end{tabular} & **Poisson’s Ratio** & **Cohesive/kN\(\cdot\)m\({}^{-2}\)** & **Friction Angle\({}^{/5}\)** \\ \hline Surrounding rock [34] & 20.0 & 1.0 & 0.38 & 125 & 23.0 \\ Reinforcement area [34] & 20.0 & 1.0 & 0.38 & 150 & 27.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical and mechanical parameters of the surrounding rock and the reinforcement area.  
Figure 9: Numerical model of shallow unsymmetrical loading section of super-large-span tunnel.
# The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation
## 2 Data
### Ancillary Data
#### 2.2.2 Infrared Atmospheric Sounding Interferometer  
IASI Level 1C radiance data is available from EUMETSAT. IASI is a hyperspectral infrared sounder which is flown onboard the Meteorological Operational satellite programme (MetOp) satellites under EUMETSAT and the European Space Agency in the local morning orbit.
## 1 Introduction  
In 2003, Project ECHO (Extension for Community Healthcare Outcomes) was launched at the University of New Mexico, Albuquerque (UNM), as a strategy for providing effective hepatitis C care for thousands of distant patients spanning New Mexico with only two specialty treatment sites in the state [1]. Project ECHO convened videoconference consultations between remote primary care physicians across New Mexico and a UNM-based hepatitis C specialty team to offer expert clinical guidance. This innovative multidisciplinary consultation system produced treatment outcomes equivalent to patients seen using traditional in-person appointments at the UNM HCV clinic [2].  
This ECHO system has been leveraged to better meet the healthcare needs of chronically underserved populations [3], currently offering successful clinical consultations for nearly 30 disease specialties to providers spanning 22 states and five countries [4, 5, 6, 7, 8].  
In 2011, the Veterans Health Administration Office of Specialty Care contracted with the ECHO team at UNM to provide guidance in establishing SCAN-ECHO (Specialty Care Access Network-Extension for Community Healthcare Outcomes), a VA implementation of the ECHO videoconference provider-to-provider consultation system, linking 10 VA Medical Center pilot sites to their corresponding satellite remote Community-Based Outpatient Clinics (CBOCs). In the same year, the Institute of Medicine released a report indicating that an estimated 100 million Americans suffered from chronic pain [9]. This report found one certified pain medicine specialist for every 28,500 people with the diagnosis. The shortage of providers has led to uneven and fragmented care, possibly causing inappropriate opioid prescribing and over utilization of interventional procedures. The use of SCAN-ECHO within the closed VA health system was considered because technology could be leveraged to deliver pain specialty care to providers in non-urban settings, where the need was most apparent.  
The prevalence of chronic pain in the veteran population is estimated to be 43% [10], and the use of this technology would allow for ongoing training of primary care providers to deliver similar care to that offered in an academic setting. Further, the number of returning veterans from wars in Iraq and Afghanistan now exceeds 2.2 million troops with over 48,000 injuries documented. Many of the injuries have led to complex medical issues that include pain as a principal diagnosis. With this in mind, as well as the undersupply of pain specialists, the goal of SCAN-ECHO was to deliver veteran-centered care to the community in which the veteran lived. It also afforded a means of avoiding long trips for the patient, improving primary care delivery of pain care and improving dialogue between primary and specialty care within the VA.
## 5 Discussions
### Cornering Stiffness
#### 5.1.1 Constant Radius Cornering Test: Understeer Coefficient (Steady-State Response)  
Simulated constant radius cornering tests were performed at speeds of 20-80 km/h using the three different values of the cornering stiffness defined in Table 6. For each test, the understeer coefficient, \(K_{us}\), was calculated using Method 3, as described earlier. The corresponding results are presented in Table 7 and Figure 11.  
As the cornering stiffness decreases, the tire slip angle intuitively increases. To maintain a constant turning radius, the steering wheel angle must therefore be increased. Consequently, the understeer coefficient of the tire also increases. In other words, for a given speed, the effects of the different cornering stiffness values on the understeer coefficient are ranked as follows: Type 3 \(>\) Type 1 \(>\) Type 2.
# A New Method of Fuzzy Support Vector Machine Algorithm for Intrusion Detection
## 1 Introduction
The design of the membership function is the key of the whole fuzzy algorithm and is no uniform criterion to be followed. At present, there are many ways to construct a membership function. Most of these methods are based on the distance between a sample and its cluster center. The closer the sample is to the cluster center, the greater the weight coefficient is. The noises and SVs distributed on the boundary are far from the cluster center. They are all given less weight coefficients. This is quite different from the objective situation. SVs should be given greater weight coefficients. To solve the above problem, our paper proposed a new fuzzy support machine method based on support vector data description (SVDD). The contributions of our work are as follows:  
* Our paper proposed a new fuzzy membership function which can effectively distinguish the noises and SVs distributed on the boundary based on SVDD. SVs are given larger weight coefficients while noises are given smaller coefficients. In this way, our method avoids imperfection of FSVM based on the distance between a sample and its cluster center. Such a fuzzy membership function structure method is more in line with reality. * The method proposed in our paper uses the hyperplane, which passes through the cluster center and takes the line of two cluster centers as the normal vector to replace each cluster center. This is in accordance with the geometric principle of SVM. In other words, two hyperplanes with maximum space are used to separate the training samples. Therefore, using the hyperplane in class to replace the cluster center can better approximate the actual situation. * Our method is more efficient, especially for anomaly-based IDS with high real-time requirements. In our method, the noises and outliers are identified by a sphere with minimum volume while containing the maximum of the samples. Some uncontrolled vectors are eliminated by pre-extracting the boundary vector set containing the support vectors. This reduces the number of training samples and speeds up the training. It is important that IDS speed up detection as much as possible by reducing computation and storage. The remainder of the paper is organized as follows. In Section 2, the previous work based on system calls are reviewed, while Section 3 describes the proposed method. In Section 4, experiments and evaluations are presented. Section 5 gives some conclusions.
## 3 Results
### L. india Growth and pH Evolution under Magnetic Field
The same conclusions can be made for the growth rates, which were found to be about 52% and 56% greater for cultures conducted under 1 h and 24 h per day of MF treatment, respectively, as well as a reduction in the doubling time by about 35% compared with the control. The pH was monitored daily and increased during all days of cultivation. The control culture varied from an initial pH of 9.72 up to 10.68 for the control assay, 9.74 up to 11.2 for the 24 h per day MF application and 9.73 up to 11.14 for 1 h per day MF application. During the cyanobacteria culture, the pH gradually increased due to the mechanism of inorganic carbon assimilation. At the initial pH, the inorganic carbon is mainly in the HCO\({}_{3}\)- form. These bicarbonate ions can enter the cells via an active transporter before being internally converted as CO\({}_{2}\) by carbonic anhydrase. Hydroxyl ions are thus released, leading to an increase in pH in the medium [39]. At a high pH, the carbon species equilibrium is modified, with an increase in CO\({}_{3}\)\({}^{2-}\) the concentration and a decrease in the HCO\({}_{3}\)- concentration (pKa = 10.3). In our experiments, the pH increased above this value, thus probably leading to a limitation in the carbon availability for all conditions. This may explain the entry into the stationary phase of the control assay, as already observed by [40] for _Spirulina_ cultures with a stop in the growth observed in the pH range of 10.5-10.8. However, while using the MF, the final pH was found to be greater, suggesting a lower carbon limitation or a better uptake efficiency of the cells. Several studies suggested that an MF could play a role in the physicochemical properties of aqueous electrolytic solutions [41; 42]. In particular, [43] proposed that MF has an impact on CO\({}_{2}\) solubility in seawater, with it being increased for greater MF intensities. Those experiments were conducted with different kinds of magnetic fields and intensities, making them difficult to compare, and this study is controversial [44]. Even if it remains to be verified, the application of MF in our experiments may thus have increased this CO\({}_{2}\) solubility, allowing for delaying the carbon limitation and supporting growth for an extended period. Nevertheless, the hypothesis of a better carbon uptake efficiency should also be considered. According to [45], an MF can affect the bioelectrocatalytic transformations of several enzyme sets in algal cells through enhancing electron transfer. These intracellular biochemical reactions could have a further impact on the microalgae cultures, modifying the CO\({}_{2}\) biofixation rate [23] and O\({}_{2}\) production rate [38; 45], as well as increasing the surface area of chloroplasts and thylakoids [34].
## 3 Results  
### Extraction Methods  
Two volatile extraction methods, SPE and SPME, were employed in this study. SPE resulted in a higher baseline and higher noises in the mass spectrum. Compared to SPE, headspace sampling using SPME showed good sensitivity when coupled with GC-QTOF-MS, as the peaks were saturated in splitless mode in our preliminary tests. So, the split ratio was changed to 1:10 for SPME-GC-QTOF-MS later, which made the peaks sharper in shape (Figure S1, supplementary figure). SPME also resulted in a cleaner baseline, which was expected because SPME only sampled the volatiles in the headspace and no solvent was injected. The relative peak areas (Table 2 and Figure S1) show that SPME favored adsorption of low molecular weight volatiles. SPE showed a good performance on the semi-volatile compound extraction, but resulted in omission of many highly volatile compounds due to the solvent delay as well as the loss during extraction. Nevertheless, both extraction methods introduced interferences from the handling materials, such as naphthalene and dibutyl phthalate in the chromatograph. For SPME, more contaminants were found mainly due to the siloxane peaks from the fiber coating. These peaks were excluded in the identification results.