esa-sceva/satcom-chunk-collection huggingface dataset:
# Drought: A Common Environmental Disaster
## 4 Remote Sensing and Drought Monitoring
There was a limited agreement between the NDVI-derived VCI and other satellite-derived drought indices and station-based drought indices over Mongolia's desert and desert steppe regions [94]. In India, where the NDVI-derived VCI alone was proven to be insufficient for drought monitoring [95], a similar issue was observed. The NDVI-derived VCI was also evaluated in other Texas counties, with varying degrees of connection between the NDVI-derived VCI and in situ drought indices [96]. Drought research that uses numerous data sources can provide multiple lines of evidence and improve the analyses' robustness. Sensors from various equipment observe the Earth's surface features in different ways, enabling the analysis of data from a range of sources, which allows for cross-validation and a better depiction of forecast uncertainty [70; 76]. Recent advancements in both new sensors and improved observational techniques, such as space-borne solar-induced chlorophyll fluorescence [67; 70], light detection and ranging (LiDAR) and hyperspectral sensors ([PERSON] et al., 2019; [PERSON] et al., 2019), provide complementary information that can be integrated into multi-sensors [67]. Space-based or remote-sensing technology has made it possible to monitor drought, soil moisture and the condition of vegetation across large areas.
###### Abstract  
Reverberation and background noise are common and unavoidable real-world phenomena that hinder automatic speaker recognition systems, particularly because these systems are typically trained on noise-free data. Most models rely on fixed audio feature sets. To evaluate the dependency of features on reverberation and noise, this study proposes augmenting the commonly used mel-frequency cepstral coefficients (MFCCs) with relative spectral (RASTA) features. The performance of these features was assessed using noisy data generated by applying reverberation and pink noise to the DEMoS dataset, which includes 56 speakers. Verification models were trained on clean data using MFCCs, RASTA features, or their combination as inputs. They validated on augmented data with progressively increasing noise and reverberation levels. The results indicate that MFCCs struggle to identify the main speaker, while the RASTA method has difficulty with the opposite class. The hybrid feature set, derived from their combination, demonstrates the best overall performance as a compromise between the two. Although the MFCC method is the standard and performs well on clean training data, it shows a significant tendency to misclassify the main speaker in real-world scenarios, which is a critical limitation for modern user-centric verification applications. The hybrid feature set, therefore, proves effective as a balanced solution, optimizing both sensitivity and specificity.  
speaker recognition; data augmentation; noise; reverb; MFCC; RASTA; speaker verification; SVM +
Footnote †: journal: Applied sciences  
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Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Related Works
### DRL-Based Coding Scheme Selection  
In the face of the rapid development of multi-user and multi-antenna communications, the conventional table look-up method [15; 16] with BER and PER as criteria is no longer applicable. [PERSON] et al. [31] use the Q-learning method for channel estimation and selection of coding schemes. This article introduces reinforcement learning into AMC, which solves the drawback that the table look-up method occupies large memory, cannot adapt to changing channels, and cannot identify continuous state and action spaces. [PERSON] et al. [33] add neural network responsible for exploration of the Q-learning framework to avoid over-exploration of the unsuitable parameter space. As fewer parameter spaces are explored, Q-learning converges faster and consumes less energy.
# A Decoding Algorithm for Convolutional Codes
## 2 Preliminaries and Notations  
In this section, we introduce some notations and recall basic notions about linear and convolutional codes that will be useful in the following sections. A detailed exposition on the algebraic theory of error-correcting codes can be found in many references (for instance, see [1]).
## 2 Tools and Methods
### Model Assessment Metrics
#### 2.2.2 Bayesian Information Criterion (BIC)  
The Bayesian Information Criterion (BIC) is a powerful model selection tool that emphasises simpler models when working with large datasets. When combined with other metrics, it provides useful insights for statistical analysis and modelling. When the true model is among the possible models, the BIC selects the true model with a probability close to 1, ensuring the selection of the correct model as the sample size increases [32].  
\[BIC=kn(n)-2\ln\left(L\right), \tag{13}\]  
where \(n\) is number of values, \(k\) is the total number of parameters estimated in the model and \(L\) is the maximum value of the model's likelihood function.
## 3 Cascade Experiments
### Transonic Cascade  
Looking for some further validation for the profile loss on the higher subsonic range, some transonic cascade tests [23; 24; 25] carried out at Carleton University were examined. In this case the compressibility effects have to be taken into account on the circulation parameter calculation. Reynolds and Mach number effects on the cascade losses had to be evaluated as well. The tested cascade design data are given in Table 1. Cascades named _HS2_, _HS1A_, _HS1C_ and _HS1D_ are rotor cascades, while _E1S50_ and _E2S50_ are nozzle cascades. Cascades _HS1A_, _HS1C_ and _HS1D_ are all designed for the same velocity diagram, and they are built with the same axial solidity but with different blade profiles. The resulting surface velocity distribution shows that _HS1A_ is mid-loaded, _HS1C_ is att-loaded and _HS1D_ is front-loaded.  
The comparison between the published measurement results [23; 24; 25] and the simulated results is given in terms of total pressure profile loss coefficient \(Y_{p}\) versus downstream flow Mach number \(M_{2}\) in Figures 3-5. For all the presented cases a fair agreement were found between simulated and experimental results in the subsonic exit Mach number range.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Cascade** & **HS2** & **HS1A** & **HS1C** & **HS1D** & **E1S50** & **E2S50** \\ \hline solidity-\(\sigma\) & 1.408 & 1.414 & 1.387 & 1.486 & 1.474 & 1.519 \\ inlet flow angle-\(\lambda_{1}\) & 120\({}^{\circ}\) & 136\({}^{\circ}\) & 136\({}^{\circ}\) & 136\({}^{\circ}\) & 96\({}^{\circ}\) & 108\({}^{\circ}\) \\ exit flow angle-\(\lambda_{2}\) & 22\({}^{\circ}\) & 31.5\({}^{\circ}\) & 31.5\({}^{\circ}\) & 31.5\({}^{\circ}\) & 30.3\({}^{\circ}\) & 40.4\({}^{\circ}\) \\ trailing edge blockage & 0.181 & 0.079 & 0.079 & 0.079 & 0.021 & 0.045 \\ \hline \end{tabular}
\end{table}
Table 1: Design data of six transonic cascades tested at Carleton University [23; 24; 25].  
Figure 3: Comparison of simulated and experimental total pressure profile loss coefficient for (**a**) HS2 cascade [23]; (**b**) HS1A cascade [24].  
Figure 2: Matching of cascade experiments [15] to momentum thickness correlation [14] by means of an equivalent diffusion factor based on Equation (3).  
The simulation was limited to subsonic exit Mach number because a particular cascade loss in the transonic regime cannot be reliably predicted by the conventional correlations [26]. In fact these losses are related to a trailing edge shock pattern and its interaction with boundary layers, which strongly depends on the detailed blade geometry [27; 28; 29].
## 4 Discussion  
### Data Comparison  
The results of this study were similar to those of previous studies (see Table 4). [PERSON] _et al._[66] showed the vegetation NPP density and NEP density, which comprised broad-leaved Korean pine forest in the Changbai Mountains, was lower than in this study because the altitude was much higher and the temperature was lower than in the PRD. The similar conditions in Russia also confirmed this similarity [58]. However, the vegetation NEP density in Italy was higher than in this study. This was due to the higher photosynthetic intensity of vegetation, which was mainly composed of forests in Italy. The density of vegetation oxygen emissions in Guangzhou, estimated using the NPP by [PERSON] _et al._[17], was higher than in this study because the oxygen consumption by heterotrophic respiration of soil organic matter was not deducted. The study of the vegetation oxygen emission density in Xiamen and the PRD indicated similar results [25,27], as well as the NEP of loblolly pine forests in California [67]. [PERSON] and [PERSON] [68] reported that the NPP gradually decreased from southeast China to northwest China, as the vegetation NPP density in the subtropical zone was more than 500 g C m\({}^{-2}\) a\({}^{-1}\), while in Hainan, Taiwan, Yunnan, and other hydrothermal locations it could exceed 1000 g C m\({}^{-2}\) a\({}^{-1}\). In this study, the vegetation NPP density in the PRD was 937 g C m\({}^{-2}\) a\({}^{-1}\), which was consistent with the results of [PERSON] and [PERSON] [68].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Study Area**} & **Vegetation NPP Density** & **Vegetation NEP** & **Vegetation Oxygen Emission** \\  & **(g C m\({}^{-2}\) a\({}^{-1}\))** & **Density(g C m\({}^{-2}\) a\({}^{-1}\))** & **Density (g O m\({}^{-2}\) a\({}^{-1}\))** \\ \hline Broad-leaved Korean pine forest in the & & & \\ Changbai Mountains [66] & 870 & 270 & - \\ Xiamen [25] & - & - & 1698 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of oxygen emissions reported in the literature.
## 3 Results and Discussions
### The pH of Concrete Cover for Corrosion Initiation  
The corrosion initiation of the rebar started from the corrosion of the ribs because the carbonation front is always the first to reach the surface of the rib. In this paper, it is argued that the corrosion initiation of the rebar does not show corrosion-induced cracks on the cover, and only the transverse/longitudinal ribs of rebars corroded (Figure 5b). Thus, the pH around a rebar is the pH value for corrosion initiation of rebars embedded in concrete, i.e., the pH of Sample B6 is 12.01.  
To determine the pH of concrete cover for the corrosion initiation of rebars embedded in concrete, the same method of analysis as that of Sample B6 in Section 3.3 was used to analyze other uncracked samples with minor corrosion. A combination analysis of in-situ inspection results, the pore solution pH test results, and XRD results were considered. It was found that the corrosion initiation of rebars embedded in concrete can occur when the pH is in the range of 11.3-12.1, as shown in Figure 12.  
To obtain the critical pH of the fully carbonated zone, the same analysis procedure as that completed for Sample C1 in Section 3.3 was used to analyze other cracked samples with severe corrosion. The concrete is fully carbonated when the pH value has a pH of 8.0-9.5, as shown in Figure 13. This is in agreement with previous test results [19; 44].  
Figure 12: pH for corrosion initiation of rebars embedded in concrete.  
The in-situ inspection results indicate that the rebar would begin to corrode when the carbonation depth failed to reach its surface. Variation in pH value in the carbonated concrete cover is shown in Figure 14. The pore solution pH test results and XRD analysis indicate that a semi-carbonated zone exists between the fully carbonated zone and the uncarbonated zone. The semi-carbonated phenomenon is due to the carbonation reaction rate of concrete being lower than the rate of carbon dioxide diffusion.  
The pH at the interface of fully carbonated and semi-carbonated zones is considered to be the lower limit of the semi-carbonated zone, and the pH value at the rebar is in the upper pH value limit. Thus, the boundary of the semi-carbonated zone is at pH values ranging from 9.5 to 12.1. This is a little higher than the previous test results from an accelerated carbonation process [45], and its pH of the semi-carbonated zone is in the range of 9.0 and 11.5. This is most likely due to the higher carbonation rates by using a higher carbon dioxide concentration and controlled environment parameters (i.e., temperature, relative humidity (RH)) of accelerated carbonation on concrete [1]. As a result, the currently used carbonation coefficients of concrete in the evaluation of existing buildings, which are mostly established based on experimental results using an accelerated carbonation process, may be overestimated and not suitable for application to naturally carbonated reinforced concrete structures.  
Figure 14: Variation in pH value in the carbonated concrete cover.  
Figure 13: pH of fully carbonated concrete.
## 3 Results
### Comparison with the Microcomputer Radiation Tolerance  
We compared the radiation tolerance of the wireless module with those of two microcomputers: an AVR microcontroller (ATmega2560, Microchip Technology Inc., Chandler, AZ, USA), which is increasingly being used owing to the popularity of Arduino, and a microcomputer with a proven track record in space (H8-3048) [24].  
For the ATmega2560, we used the results obtained simultaneously in this test, and for the H8-3048, we used the results previously obtained by [PERSON] et al. [21]. Figures 10 and 11 show the SEU \(\sigma\) values corresponding to the ATmega2560 and H8-3048 \(L\) values, respectively.  
The comparison of Figures 9 and 10 reveals comparable SEU \(\sigma\) values for the wireless module and ATmega2560 at approximately 1.0 \(\times\) 10\({}^{-8}\) [cm\({}^{2}\)/bit]. However, the comparison of Figures 9 and 11 reveals that the SEU \(\sigma\) values of H8-3048 were higher than those of the wireless module; thus, H8-3048 has a lower radiation tolerance than that of the wireless module.  
Based on the above comparison, if the C1120 wireless module is to be applied to satellite components as a small communication system, a microcomputer with the same radiation tolerance level as the wireless module, such as ATmega2560, may be desirable to use as the control IC rather than a microcomputer with a lower radiation tolerance, such as H8-3048.
# Estimation of Stability Parameters for Wide Body Aircraft Using Computational Techniques
## 2 US DATCOM
### Aerodynamic Coefficients Estimation Using US DATCOM  
DATCOM treats various high lift devices, including plain, single/double slotted jet flaps; leading-edge flaps; and control devices, such as spoilers and typical wing-body-tail configuration with controlled effectiveness. The software application is well-designed for the conventional aircraft configurations [51]. The characteristics including lateral, longitudinal, and directional stability obtained from DATCOM are in the stability axis system. For specific configurations and Mach number regimes, coefficients, e.g., Cl\(\beta\), Cm\(\alpha\), Cn\(\beta\), CL\(\alpha\), CL, Cm, and CD, can be obtained from the DATCOM output file. Additionally, parameters such as lift, roll, moment, normal dynamic derivatives, side-force for the angle of attack, and angular rates can also be computed [48]. Flight conditions are defined in DATCOM based on Reynolds number and Mach number data. This condition can be satisfied by defining velocity, altitude, temperature, and pressure along with the requisite Reynolds number and Mach number. In the present study, aerodynamic coefficients of the Boeing 747-200 airplane at low-cruise-flight conditions (M = 0.5, Alt = 20,000 ft, and \(\alpha\) = 6.8 deg) were estimated.
## 4 Discussion
### Advantages of Developed CET
#### 4.1.3 Wide Range of Other Potential Applications  
Besides coastline mapping, CET can be potentially applied for a wide range of applications. During the automated extraction of the coastline, the CET managed to detect various other objects that can provide potentially valuable information for different applications (Figure 14). For example, the CET has successfully managed to detect very small objects, such as small fishing boats (Figure 14A), or even fast-moving objects, such as speedboats (Figure 14C), and small low-flying aircraft (Figure 14D). Furthermore, CET has successfully mapped most of the existing fish farms within the study area (Figure 14B). The ability to locate and detect such small and fast-moving objects can be crucial for monitoring and protecting coastal areas [75; 76; 77; 78]. Automated detection of small fishing vessels can be used for the prevention and control of illegal fishing or can be potentially used for easily and quickly locating missing boats during search and rescue missions. Given the very high temporal (revisit time of 1.1 days [79]) and spatial resolution of WV imagery, the application of CET can provide effective management over very large coastal areas.
## 2 Leakage Point Detection and Groundwater Level Monitoring  
### Spot Detection of Foundation Pit Leakage  
The fiber optic temperature measurement system was set up by using DTS-10 (Manufactured by Beijing TOP Photonics Co., Ltd.) copper network internal heating temperature sensing fiber optic cable and OFDR (OSI-C) high precision distributed optical frequency domain strain temperature analyzer [28], as shown in Figure 1. The fiber optic temperature measurement system was used after the construction of the foundation pit enclosure structure is completed (before the excavation of the foundation pit). Details of fiber optic deployment and on-site detection are shown in Figure 2. OFDR (OSI-C) analyzer can achieve a spatial resolution of 1 mm within 50 m sensing range and a resolution of 1 cm within 100 m sensing range, which guarantees high precision detection of leakage points in this project.  
The optical fiber was heated using 8 W power. During the heating process, the temperature change at different locations and depths of the enclosure structure were obtained. Then the data were analyzed to obtain the variation curve of fiber temperature with depth and location.  
Figure 1: The photo of temperature measurement fiber and temperature analyzer.
# Partitioning of Net Ecosystem Exchange Using Dynamic Mode Decomposition and Time Delay Embedding +
## 1 Introduction  
Carbon losses from ecosystems affect climate change. Ecosystem respiration (Reco), the sum of autotrophic and heterotrophic respiration, represents a major component of the global carbon cycle. Accurate estimation of Reco dynamics is necessary for a better understanding of ecosystem-climate interactions and the impact of climate extremes on ecosystems. This paper proposes a new data-driven method for the estimation of the nonlinear dynamics of Reco using the method of dynamic mode decomposition (DMD), an emerging tool for the analysis of nonlinear dynamical systems.  
Ecosystem respiration is typically described as an exponential function of temperature based on the law of thermodynamics [1]. This function is defined based on certain parameters, such as temperature sensitivity, which are assumed to remain constant. However, several studies have pointed to the dependence of these parameters on other drivers of Reco [2]. Such issue is partially compensated in regression models by the use of temporal moving windows for parameters estimation [3].  
The Eddy Covariance (EC) technique is widely used to measure the net ecosystem exchange (NEE) which is the difference between Reco, the total CO2 release due to all respiration processes, and the gross carbon uptake by photosynthesis (GPP). The two CO2 fluxes Reco and GPP are derived from NEE by applying partitioning methods. Recentlydeep learning-based methods have been proposed for modeling Reco dynamics [4; 5] using EC measurement of night-time NEE when photosynthesis, and therefore GPP, is assumed to be 0. These approaches provide data-driven equation-free estimates of Reco with the flexibility to include other meteorological and biological drivers affecting Reco during the daytime to achieve the NEE partitioning task. In spite of their improved performance compared to state-of-the-art empirical methods, they require a sufficient amount of training data as well as extensive tuning of the used deep networks' hyper-parameters. Accordingly, the trained model cannot take into account some short-term variations in ecosystem respiration.
## 4 Biological Activities of _Passiflora caerulea_ L.
### Antioxidant Activity  
The antioxidant activity of _Passiflora caerulea_ has been pointed out as a significant biological property, essential for neutralizing reactive oxygen species, potentially reducing inflammation and cellular damage and having a positive effect against oxidative stress-related disorders. Earlier studies have shown that the phytochemical components of the plant, including the flavonoids, phenolic compounds and alkaloids, play a crucial role in its antioxidant capabilities. Many studies have underscored specific findings, further highlighting its therapeutic significance in addressing oxidative damage [19; 23; 25; 33].  
According to [PERSON] and [PERSON], _P. caerulea_ leaf extract has been found to possess high antioxidant activity in five different assays [23]. [PERSON] et al. investigated this potential and observed rapid discoloration of DPPH and ABTS radicals, which they attributed to the presence of strong antioxidant reductants in passion fruit extract. The results for the _P. caerulea_ extract have also indicated inhibitions of 80.15% and 77.85% in NO and LPO assays, respectively [19]. A study by [PERSON] et al. demonstrated that the antioxidant activity of the leaf extracts is higher than that of the pulp [17]. According to the DPPH, FRAP and CUPRAC methods, the extracts with 70% ethanol in the leaves have the highest values, while in the ABTS method, the 50% ethanol has shown the highest activity. This is consistent with the findings of [PERSON], who indicated that the ethanol extract of _P. caerulea_ leaves exhibited the highest antioxidant activity against DPPH radicals, with an IC\({}_{50}\) value of 54.01 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, comparable to the standard antioxidant BHT (IC\({}_{50}\) = 57.16 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) [33]. Similar outcomes were achieved for the antioxidant capacity through the ABTS radical scavenging assay. In a in vivo experiment by [PERSON] et al., the ethyl acetate fraction of _P. caerulea_ leaves demonstrated an antioxidant potency of 71% relative to vitamin E [9].