esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
In this section, the implementation of the proposed method is illustrated concretely. The overall framework of the proposed method is shown in Figure 1. The figure includes a preprocessing step and three main parts from left to right.  
The preprocessing takes the SAR image as the starting point, solves the pixel similarity based on Gamma distribution by the non-negative logarithmic likelihood difference, and generates the superpixel mapping, and the specific description is shown in Section 3.1.  
The three bodies are the feature space construction, adaptive kernel combination, and conflict resolution. Feature space construction is carried out on three group texture information feature tensors from the SAR image, respectively, which are gray-level co-occurrence matrix (GLCM), wavelet (WL), and attribute profile (AP) from top to bottom. Each feature tensor is clustered through the superpixel mapping to obtain 3D adaptive blocks. Based on these blocks, the internal context information feature space and the generalized space information feature space are constructed, as detailed in Section 3.2.  
Figure 1: Framework of the proposed method.  
In the adaptive kernel combination stage, the texture feature kernel, contextual kernel and spatial kernel corresponding to three feature spaces in one group are adaptively combined through dynamic weight to obtain a composite kernel, which realizes information fusion within features of one group. Dynamic weights according to image content rather than artificial setting combine feature kernels, which reflect the feature difference to mine the spatial information. The stage is described in detail in Section 3.3.  
Conflict resolution is the decision embodiment of local consistency optimization, and the classification maps generated by three composite kernels accordingly are considered to enhance the local consistency smoothing labels, which realizes complementary information fusion among features in different groups, and the final classification result is obtained. See Section 3.4 for detailed description.
# A Review of AlGaN-Based Deep-Ultraviolet Light-Emitting Diodes on Sapphire
## 3 Crystal Quality of AlGaN on AlN  
The TDD has a strong relationship with NRCs [77] for AlGaN QWs. To clarify the crystal quality required for AlN, the AlGaN QWs and n-AlGaN cladding layer must be discussed together. The EQEs of bare die LEDs on sapphire and AlN bulk substrates in Figure 1 do not substantially differ. It has been claimed that 2 \(\times\) 10\({}^{8}\)/cm\({}^{2}\) is good enough to obtain a satisfactory IQE [24; 41] for the flat QWs, showing AlN bulk substrate with TDD of less than 10\({}^{6}\)/cm\({}^{2}\) is over quality. However, the AlN template with TDD on the order of 10\({}^{8}\)/cm\({}^{2}\) to grow AlGaN LED layers was difficult to be fabricated on sapphire. In addition, misfit dislocations at the interface with AlN between AlGaN layer increased the TDD of the n-AlGaN cladding layer [78; 79] in the case of using AlN bulk substrate with the TDD of less than 10\({}^{6}\)/cm\({}^{2}\), showing that n-Al\({}_{0.5}\)Ga\({}_{0.5}\)N cladding layers exhibited (10-12) and (0002) \(\upomega\)-scan X-ray rocking curves (XRCs) with full-width-half-maximum (FWHM) of greater than 600 arcs and 200 arcs, respectively [80]. Except in the case of AlGaN with a high AlN content, the TDD of n-AlGaN layer on AlN or AlN templates is considered to be limited to the order of 10\({}^{8}\)/cm\({}^{2}\) when AlGaN is grown on a planer AlN surface.  
The required crystal quality should be discussed on the basis of point defects at the AlGaN QWs as well as the TDD. When the crystal quality is moderately improved, NRCs derived from point defects must be considered [81] because Al vacancies (V\({}_{\text{Al}}\)) have been revealed to be NRCs in AlGaN QWs [82]. Carrier localization has been clarified to be the reason why GaN with a high TDD of between 10\({}^{8}\)/cm\({}^{2}\) and 10\({}^{9}\)/cm\({}^{2}\) enables the mass production of InGaN-LEDs [83; 84; 85]. Similarly, AlGaN-LEDs are considered to involve the carrier localization effect [86; 87; 88; 89]. The effects of V\({}_{\text{Al}}\) on IQE have been observed using samples with TDDs on the order of 10\({}^{9}\)/cm\({}^{2}\)[90].  
Until about 2005, AlN on sapphire showed insufficient crystallinity to enable the growth of AlGaN LEDs, making it impossible to realize the practical efficiencies. The difficulty of improving AlGaN quality when AlN crystallinity is insufficient is due to crystal improvement mechanisms such as three-dimensional growth are not expected for AlGaN on AlN. Attempts to overcome this problem to obtain AlN with a moderate TDD on the order of 10\({}^{8}\)/cm\({}^{2}\) on sapphire substrate are described in the following.
## 2 Problem and the Background  
A number of methods have been used for solving lithology classification and interpretation problems, such as cross plot interpretation and statistical analysis based on histogram plotting [5], support vector machine (SVM) using traditional wireline well logs [6], fuzzy-logics (FL) for association analysis, neural networks and multivariable statistical methodologies [7], artificial intelligence approaches and multivariate statistical analysis [8], hybrid NN methods [9], self organized map (SOM) [10], FL methods [11], artificial neural network (ANN) methods [12; 13], lithology classification from seismic tomography [14], multi-agent collaborative learning architecture approaches [15], random forest [16; 17], generative adversarial network [18] and multivariate statistical methods [19].  
Numerous mathematical approaches have been introduced recently to automate the process of identifying and classifying electrofacies. In several researches the general information about lithology and rock properties changes are extracted from the electrofacies for pattern recognition [20; 21; 22]. The researches mainly consist of applying discriminant analysis, principal components analysis (PCA), and cluster analysis [20; 21].  
To classify electrofacies, numerous other approaches are shown in the literature [22; 23; 24; 25; 26]. The majority of the available commercial software packages and approaches for subsurface modeling include electrofacies functions. However, the explanations of how these functions generate the results are rarely explained and the processes operate as \"blackboxes.\" In [27], the feedforward neural network and clustering are used for the determination and classification of electrofacies, which are also black-box approaches.  
The performance of the ANN and FL methods are superior compared with statistical methods [6; 7; 12; 13; 19; 28]. SOM methods provide better results in lithology classification compared to other machine learning techniques [29]. Other kinds of NN are faster than probabilistic neural networks (PNN), because PNN involves more computational steps [19].  
Recently, rule-based whitebox classification modules such as RST has been used in several related areas for solving classification problem analysis [30; 31]. [PERSON] at al. [32] have used non-deterministic apriori rules to identify lithology classes directly from well logs.
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 5 Evaluation
### Frequency Band Consumption Comparison  
Using the measured energy consumption values for both \(868\,\mathrm{MHz}\) and \(2.4\,\mathrm{GHz}\), we conducted several TSCH network simulations to analyze the end-to-end network performance and energy consumption at these frequency bands.
## 3 Results and Discussion
### Cheminformatics Prediction
#### 3.2.4. ADMET Prediction
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**Mutagenicity**} & \multirow{2}{*}{**Tumorigenicity**} & \multirow{2}{*}{**Reproductive**} & \multirow{2}{*}{**Irritating Effect**} & \multicolumn{4}{c}{**Toxicity *} \\ \cline{4-9}  & & & & & & **hERG** & **H-HT** & **AMES** & \(\mathbf{LD_{50}}\) \\  & & & & & & & & **mg/kg** \\ \hline
5 _anti_ & None & None & None & None & \(-\)0.282 & \(+\)0.564 & \(+\)0.676 & 802.40 \\
5 _syn_ & None & None & None & None & \(-\)0.261 & \(-\)0.414 & \(+\)0.676 & 811.69 \\
6 _anti_ & High & None & High & High & \(+\)0.761 & \(+\)0.756 & \(-\)0.31 & 388.28 \\
6 _syn_ & High & None & High & High & \(+\)0.758 & \(+\)0.774 & \(-\)0.31 & 4122.24 \\
7 _anti_ & None & None & None & None & \(+\)0.66 & \(-\)0.484 & \(-\)0.276 & 262.66 \\
7 _syn_ & None & None & None & None & \(+\)0.672 & \(+\)0.508 & \(-\)0.276 & 249.11 \\
**Maprotiline 9** & None & None & High & None & \(+\)0.879 & \(+\)0.878 & \(-\)0.282 & 912.27 \\ \hline hERG: Human ether-a-go-go related gene; H-HT: Human Hepatotoxicity; AMES: Ames Mutagenicity; LD50: Median \\ lethal dose;* calculated by ADMETlab server. & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Toxicity risk assessment of the compounds **(5–7) _anti_**, **(5–7) _syn_**, and maprotiline **9** using Data Warrior and ADMETlab server *. From a qualitative point of view, the compounds **5** _anti, **5** _syn_, **7** _anti,_ and **7** _syn_ exhibit no mutagenicity, no tumorigenicity, no reproductive effect, and also have no irritability toxicity. The reference drug **9** shares with compounds **6** _anti_ **and **6** _syn_ **in their excreting reproductive toxicity. Furthermore, compounds **6** _anti_ **and **6** _syn_ **revealed mutagenicity and irritability toxicity**
###### Abstract  
Model-based 3D/2D image registration using single-plane fluoroscopy is a common setup to determine knee joint kinematics, owing to its markerless aspect. However, the approach was subjected to lower accuracies in the determination of out-of-plane motion components. Introducing additional kinematic constraints with an appropriate anatomical representation may help ameliorate the reduced accuracy of single-plane image registration. Therefore, this study aimed to develop and evaluate a multibody model-based tracking (MbMBT) scheme, embedding a personalized kinematic model of the tibiofemoral joint for the measurement of tibiofemoral kinematics. The kinematic model was consisted of three ligaments and an articular contact mechanism. The knee joint activities in six volunteers during isolated knee flexion, lunging, and sit-to-stand motions were recorded with a biplane X-ray imaging system. The tibiofemoral kinematics determined with the MbMBT and mediolateral view fluoroscopic images were compared against those determined using biplane fluoroscopic images. The MbMBT was demonstrated to yield tibiofemoral kinematics with precision values in the range from 0.1 mm to 1.1 mm for translations and from 0.2\({}^{\circ}\) to 1.3\({}^{\circ}\) for rotations. The constraints provided by the kinematic model were shown to effectively amend the nonphysiological tibiofemoral motion and not compromise the image registration accuracy with the proposed MbMBT scheme.  
Keywords:model-based tracking; image registration; fluoroscopy; knee; kinematic model +
Footnote †: journal: Medical  
==WARNING: Truncated because of repetitions==
)0.1 & 0.8862 & 3.0 & \(\pm\)1.1 & 1.0 & \(\pm\)1.9 & 2.9 & \(\pm\)1.0 & 0.3066 \\ \hline MMMET & 1.0 & \(\pm\)0.4 \({}^{*}\) & 0.5 & \(\pm\)0.7 & 0.8 & \(\pm\)0.3 \({}^{*}\) & 0.9751 & 0.3 & \(\pm\)0.2 \({}^{*}\) & 0.3 & \(\pm\)0.2 \({}^{*}\) & 0.2 & \(\pm\)0.1 \({}^{*}\) & 0.9732 & 1.4 & \(\pm\)0.8 \({}^{*}\) & 1.0 & \(\pm\)1.1 & 1.0 & \(\pm\)0.3 \({}^{*}\) & 0.32
+++  
The degree of agreement between the MbMBT-determined kinematics and the reference values during the two weight-bearing activities were greater than 0.94 and 0.92, respectively, except for the LM translation (Tables 2 and 3). While the LM translation was still the least accurate component, improvements in MAD, precision and degree of agreement over the SbMBT were observed (Tables 2 and 3).
# OneMap Drought Monitoring Analysis Based on Statistical Models
## 2 Materials and Methods  
### Study Area and Data  
To develop an OneMap drought index based on existing drought indices, the spatial range was targeted at data for 167 local governments (Figure 1), and the time range was set to 2017-2020 to comparatively analyze drought forecasts and warning data for each ministry. The ground- and satellite-based drought indices were corrected, and the application data were drought forecasts and warning operation data provided by ministries.
## 2 Optical Waveguide Cross-Section Design and Simulation
### Waveguide Design for an Evanescent Field Sensor/Probe  
To achieve a highly sensitive evanescent field sensor, we wished to increase the evanescent field presence or overlap with the external medium. This can be achieved by reducing the core diameter and, subsequently, the core confinement factor of the designed single-mode waveguide. However, the supporting nano-fin also limits waveguiding via leakage to the substrate. Figure 2b showed the variation of the effective index of the waveguide as a function of the core diameter when supported by a 0.3 \(\upmu\)m wide, 1.5 \(\upmu\)m high nano-fin (along with the no-fin case for comparison). As the core diameter decreases, a larger fraction of the field is present in the surrounding refractive index of air and the effective index drops. This is better assessed using the core confinement factor as a metric, which is plotted in Figure 6. The confinement factor decreased with decreasing core diameter, reaching a minimum value of 18% and 53% for LP\({}_{01\text{x}}\) and LP\({}_{01\text{y}}\) polarizations, respectively, inclusive of the power carried by the nano-fin, hence the LP\({}_{01\text{x}}\) mode was significantly more sensitive than LP\({}_{01\text{y}}\) near the cutoff value. Without the fin, the confinement factor can reach 6% close to the cutoff at 0.5 \(\upmu\)m diameter, with the cutoff emerging from the nearby substrate. Maximum sensitivity can be achieved with lower core diameters; however, fabricating a core waveguide smaller than 1 \(\upmu\)m or a nano-fin narrower than 0.25 \(\upmu\)m is challenging with current direct laser writing technology, hence setting a technological limit to the evanescent field overlap with the surroundings.  
Figure 5: (**a**) Electric field propagation through the tapered waveguide, evolving from a 2 \(\upmu\)m to a 4 \(\upmu\)m diameter within \(\sim\)80 \(\upmu\)m. (**b**) Output mode coupling efficiency to the fundamental LP\({}_{01}\) and higher-order LP\({}_{02}\) modes, as a function of taper length.
## 4 Network Communication
### State-of-the-Art Research on Network Communication
#### 4.1.3 Physical Layer
Its large bandwidth signals provide robustness to jamming with low probability of interception. Moreover, UWB can be used to monitor vital respiration and heart-rate parameters [111, 112, 113]. In addition, UWB has good penetrating properties that could be applied to imaging in medical applications [114]. In 2007, the Sixth Working Group of IEEE 802.15 was established to standardize on BSN bands. The team eventually divided the bands into three parts: UWB, NB, and HBC [115]. MICS, WMTS, FCC, and ISM belong to NB. In addition, with the newly proposed IEEE802.11 ad standard, researchers try to present novel techniques to enable over-body propagation between 15 GHz and 40 GHz, and even the unlicensed 60 GHz region [116].
## 1 Introduction
For instance, the toxicity of pyridinium and imidazolium ILs for the alga _Selenstrum capricornutum_ was reported to be between two and four orders of magnitude higher than that of traditional organic solvents [26]. Likewise, phosphonium- and ammonium-based TSILs used in heavy metal extraction were acutely toxic to freshwater algae [13]. While toxicity depends on several factors, e.g., the alkyl chain length or the nature of the functional group [27], the results vary tremendously depending on the applied biological test [22]. These findings underpin that testing the ecotoxic potential of TSILs proposed as extracting agents is a prerequisite before use: improved extraction capabilities should not be accompanied by increased toxicity. Furthermore, these assays should be straight-forward, reproducible and comparable to each other. The recently characterized TSILs trihexyltetradylphosphonium-, methyltrioctylphosphonium- and methyltrioctylammonium 3-hydroxy-2-naphthoates displayed great potential as heavy metal extractants; the best results were achieved for Cu, Ag, Cd and Pb [28]. The performed extraction experiments with model solutions were in the mg L\({}^{-1}\) concentration range, which is commonly performed. Nonetheless, follow-up investigations using trace level concentrations are rarely reported. [PERSON] et al. [11] found a positive correlation between extraction efficacies and initial concentrationsfor several cases, emphasizing that high extraction efficacies in the mg L\({}^{-1}\) concentration range do not necessarily apply in lower concentration ranges. This highlights the importance of using trace level concentrations in extraction experiments, which are reported here for the first time for the ILs under investigation. The experiments were designed to show if environmentally safe residual concentrations after extraction could be achieved, even when initial metal concentrations were in the \(\mu\)g L\({}^{-1}\) range. For this study, we also included investigations on mercury, for which experiments have not been conducted before. To further assess their applicability with respect to toxicological concerns, the toxicological potential of the ILs towards the freshwater green algae _Raphidocelis subcapitata_ and _Tetradesmus obliquus_ was investigated in acute toxicity assays. These tests included growth inhibition assays and were enhanced by monitoring the photosynthetic performance via chlorophyll fluorescence. We propose this non-invasive and fast method as a first ecotoxicity screening of novel TSILs used as extracting agents in aqueous phases. The combined results from extraction experiments and acute toxicity assays should help to shed light on the practicability and feasibility of using highly hydrophobic ammonium- and phosphonium-based TSILs as metal extracting agents.
_Article_  
**Front-End of Vehicle-Embedded Speech Recognition**  
**for Voice-Driven Multi-UAVs Control**  
**[PERSON] 1,*(r) and [PERSON] 2**  
1 Department of English Linguistics and Language Technology, Hankuk University of Foreign Studies, Seoul 02450, Korea  
2 Department of English Linguistics, Hankuk University of Foreign Studies, Seoul 02450, Korea; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +82-02-2173-8814  
Received: 29 August 2020; Accepted: 29 September 2020; Published: 30 September 2020  
**Featured Application: This research can be applied to voice-driven control of multiple devices with device-embedded speech recognition. Such systems require efficient front-end processing, including noise reduction and voice trigger.**  
**Abstract:** For reliable speech recognition, it is necessary to handle the usage environments. In this study, we target voice-driven multi-unmanned aerial vehicles (UAVs) control. Although many studies have introduced several systems for voice-driven UAV control, most have focused on a general speech recognition architecture to control a single UAV. However, for stable voice-controlled driving, it is essential to handle the environmental conditions of UAVs carefully, including environmental noise that deteriorates recognition accuracy, and the operating scheme, e.g., how to direct a target vehicle among multiple UAVs and switch targets using speech commands. To handle these issues, we propose an efficient vehicle-embedded speech recognition front-end for multi-UAV control via voice. First, we propose a noise reduction approach that considers non-stationary noise in outdoor environments. The proposed method improves the conventional minimum mean squared error (MMSE) approach to handle non-stationary noises, e.g., babble and vehicle noises. In addition, we propose a multi-channel voice trigger method that can control multiple UAVs while efficiently directing and switching the target vehicle via speech commands. We evaluated the proposed methods on speech corpora, and the experimental results demonstrate that the proposed methods outperform the conventional approaches. In trigger word detection experiments, our approach yielded approximately 7%, 12%, and 3% relative improvements over spectral subtraction, adaptive comb filtering, and the conventional MMSE, respectively. In addition, the proposed multi-channel voice trigger approach achieved approximately 51% relative improvement over the conventional approach based on a single trigger word.  
**Keywords:** speech recognition; voice-driven control; noise reduction; voice trigger; unmanned aerial vehicle (UAV); multi-UAVs control; minimum mean squared error (MMSE)
# Time Series Clustering of Online Gambling Activities for Addicted Users' Detection
## Appendix A Time Series Analysis  
A time series is defined as a set of observations \(x_{t}\), each one being recorded at a specific time \(t\). In this paper, one observation corresponds to the profit for a single bet in the time instant in which that bet is placed by a single user.  
In general, an increasingly large part of the worlds' data is in the form of time series. Time series analysis includes methods for analyzing temporal data to discover the inherent structure and condense information over temporal data, as well as the meaningful statistics and other characteristics of the data. In the context of data mining, pattern recognition, and machine learning, time series analysis can be used for clustering, classification, anomaly detection, or forecasting. In this paper, we aim at clustering together gamblers that share common behaviors. Thus, time series clustering is the best and well-suited set of methods for our problem, since it has the goal of partitioning time series data into groups based on similarity or distance measures, so that time series in the same cluster are similar.  
One major problem that arises during time series clustering, and in general in time series analysis, is the comparison of time series or sequences. The most studied approaches rely on proximity measures and metrics. Thus, a crucial question is establishing what we mean by \"dissimilar\" data objects. This concept is particularly complex due to the dynamic character of the series, and different approaches to define the (dis)similarity between time series have been proposed in the literature [18].  
In particular, Euclidean distance and its variants, like the Minkowski distance, present several drawbacks that make their use in our application inappropriate. They compare only time series of the same length, which turns out to be unlikely due to the nature of our problem, as the number of bets varies from player to player. Additionally, they do not handle outliers or noise, and they are very sensitive with respect to six signal transformations: shifting, uniform amplitude scaling, uniform time scaling, uniform biscaling, time warping, and non-uniform amplitude scaling [19].  
For this reason, this paper relies on dynamic time warping to determine the best alignment among time series.